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    "colab": {
      "name": "03.09-Pivot-Tables.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oovdyKo-pRfY",
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      "source": [
        "<!--BOOK_INFORMATION-->\n",
        "<img align=\"left\" style=\"padding-right:10px;\" src=\"https://github.com/hdsz25/PythonDataScienceHandbook/blob/master/notebooks/figures/PDSH-cover-small.png?raw=1\">\n",
        "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
        "\n",
        "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sItfWNApRfZ",
        "colab_type": "text"
      },
      "source": [
        "<!--NAVIGATION-->\n",
        "< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) >"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21xsRen2pRfa",
        "colab_type": "text"
      },
      "source": [
        "# Pivot Tables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dIXEmvmXpRfa",
        "colab_type": "text"
      },
      "source": [
        "We have seen how the ``GroupBy`` abstraction lets us explore relationships within a dataset.\n",
        "A *pivot table* is a similar operation that is commonly seen in spreadsheets and other programs that operate on tabular data.\n",
        "The pivot table takes simple column-wise data as input, and groups the entries into a two-dimensional table that provides a multidimensional summarization of the data.\n",
        "The difference between pivot tables and ``GroupBy`` can sometimes cause confusion; it helps me to think of pivot tables as essentially a *multidimensional* version of ``GroupBy`` aggregation.\n",
        "That is, you split-apply-combine, but both the split and the combine happen across not a one-dimensional index, but across a two-dimensional grid."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s5uNag2fpRfb",
        "colab_type": "text"
      },
      "source": [
        "## Motivating Pivot Tables\n",
        "\n",
        "For the examples in this section, we'll use the database of passengers on the *Titanic*, available through the Seaborn library (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aq0ufY9cpRfb",
        "colab_type": "code",
        "colab": {
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          "height": 72
        },
        "outputId": "47b214d1-0d48-4a21-8dc7-dfa30db791af"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "titanic = sns.load_dataset('titanic')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_Z-rO-KpRfe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "ba588e46-b60d-40ea-a2c2-075063b1b1b5"
      },
      "source": [
        "titanic.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass     sex   age  ...  deck  embark_town  alive  alone\n",
              "0         0       3    male  22.0  ...   NaN  Southampton     no  False\n",
              "1         1       1  female  38.0  ...     C    Cherbourg    yes  False\n",
              "2         1       3  female  26.0  ...   NaN  Southampton    yes   True\n",
              "3         1       1  female  35.0  ...     C  Southampton    yes  False\n",
              "4         0       3    male  35.0  ...   NaN  Southampton     no   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAiOS9K7pRfi",
        "colab_type": "text"
      },
      "source": [
        "This contains a wealth of information on each passenger of that ill-fated voyage, including gender, age, class, fare paid, and much more."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMysJYPSpRfi",
        "colab_type": "text"
      },
      "source": [
        "## Pivot Tables by Hand\n",
        "\n",
        "To start learning more about this data, we might begin by grouping according to gender, survival status, or some combination thereof.\n",
        "If you have read the previous section, you might be tempted to apply a ``GroupBy`` operation–for example, let's look at survival rate by gender:"
      ]
    },
    {
      "cell_type": "code",
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          "height": 141
        },
        "outputId": "7efb72a5-e1c8-4435-fc95-b208f237d37d"
      },
      "source": [
        "titanic.groupby('sex')[['survived']].mean()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.742038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>"
            ],
            "text/plain": [
              "        survived\n",
              "sex             \n",
              "female  0.742038\n",
              "male    0.188908"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "TTzzQ2nDpRfl",
        "colab_type": "text"
      },
      "source": [
        "This immediately gives us some insight: overall, three of every four females on board survived, while only one in five males survived!\n",
        "\n",
        "This is useful, but we might like to go one step deeper and look at survival by both sex and, say, class.\n",
        "Using the vocabulary of ``GroupBy``, we might proceed using something like this:\n",
        "we *group by* class and gender, *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and then *unstack* the hierarchical index to reveal the hidden multidimensionality. In code:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPvfkLF1pRfm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "8c39b4ae-0625-4edb-ae7e-3dcd23b94368"
      },
      "source": [
        "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.968085</td>\n",
              "      <td>0.921053</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.368852</td>\n",
              "      <td>0.157407</td>\n",
              "      <td>0.135447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "class      First    Second     Third\n",
              "sex                                 \n",
              "female  0.968085  0.921053  0.500000\n",
              "male    0.368852  0.157407  0.135447"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
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      "cell_type": "markdown",
      "metadata": {
        "id": "qFfcYv3opRfo",
        "colab_type": "text"
      },
      "source": [
        "This gives us a better idea of how both gender and class affected survival, but the code is starting to look a bit garbled.\n",
        "While each step of this pipeline makes sense in light of the tools we've previously discussed, the long string of code is not particularly easy to read or use.\n",
        "This two-dimensional ``GroupBy`` is common enough that Pandas includes a convenience routine, ``pivot_table``, which succinctly handles this type of multi-dimensional aggregation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GM2e5gflpRfp",
        "colab_type": "text"
      },
      "source": [
        "## Pivot Table Syntax\n",
        "\n",
        "Here is the equivalent to the preceding operation using the ``pivot_table`` method of ``DataFrame``s:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDHYK8dHpRfp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "39f75460-aa0b-4004-b5e6-bc4caa5318cf"
      },
      "source": [
        "titanic.pivot_table('survived', index='sex', columns='class')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.968085</td>\n",
              "      <td>0.921053</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.368852</td>\n",
              "      <td>0.157407</td>\n",
              "      <td>0.135447</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "class      First    Second     Third\n",
              "sex                                 \n",
              "female  0.968085  0.921053  0.500000\n",
              "male    0.368852  0.157407  0.135447"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4SfUlUWQpRfs",
        "colab_type": "text"
      },
      "source": [
        "This is eminently more readable than the ``groupby`` approach, and produces the same result.\n",
        "As you might expect of an early 20th-century transatlantic cruise, the survival gradient favors both women and higher classes.\n",
        "First-class women survived with near certainty (hi, Rose!), while only one in ten third-class men survived (sorry, Jack!)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2c5hhSg2pRft",
        "colab_type": "text"
      },
      "source": [
        "### Multi-level pivot tables\n",
        "\n",
        "Just as in the ``GroupBy``, the grouping in pivot tables can be specified with multiple levels, and via a number of options.\n",
        "For example, we might be interested in looking at age as a third dimension.\n",
        "We'll bin the age using the ``pd.cut`` function:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEGZg1MSpRft",
        "colab_type": "code",
        "colab": {},
        "outputId": "3dd61801-03ea-4f18-d53b-dc090a2793f7"
      },
      "source": [
        "age = pd.cut(titanic['age'], [0, 18, 80])\n",
        "titanic.pivot_table('survived', ['sex', age], 'class')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>0.909091</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.511628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(18, 80]</th>\n",
              "      <td>0.972973</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.423729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.215686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(18, 80]</th>\n",
              "      <td>0.375000</td>\n",
              "      <td>0.071429</td>\n",
              "      <td>0.133663</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "class               First    Second     Third\n",
              "sex    age                                   \n",
              "female (0, 18]   0.909091  1.000000  0.511628\n",
              "       (18, 80]  0.972973  0.900000  0.423729\n",
              "male   (0, 18]   0.800000  0.600000  0.215686\n",
              "       (18, 80]  0.375000  0.071429  0.133663"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXRLJzGFpRfw",
        "colab_type": "text"
      },
      "source": [
        "We can apply the same strategy when working with the columns as well; let's add info on the fare paid using ``pd.qcut`` to automatically compute quantiles:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxNN-2PWpRfw",
        "colab_type": "code",
        "colab": {},
        "outputId": "cbcd733e-cfd9-4aa3-dbbd-df11ff64df9b"
      },
      "source": [
        "fare = pd.qcut(titanic['fare'], 2)\n",
        "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>fare</th>\n",
              "      <th colspan=\"3\" halign=\"left\">[0, 14.454]</th>\n",
              "      <th colspan=\"3\" halign=\"left\">(14.454, 512.329]</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.318182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(18, 80]</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.972973</td>\n",
              "      <td>0.914286</td>\n",
              "      <td>0.391304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
              "      <th>(0, 18]</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.260870</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>0.178571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(18, 80]</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.098039</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.391304</td>\n",
              "      <td>0.030303</td>\n",
              "      <td>0.192308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "fare            [0, 14.454]                     (14.454, 512.329]            \\\n",
              "class                 First    Second     Third             First    Second   \n",
              "sex    age                                                                    \n",
              "female (0, 18]          NaN  1.000000  0.714286          0.909091  1.000000   \n",
              "       (18, 80]         NaN  0.880000  0.444444          0.972973  0.914286   \n",
              "male   (0, 18]          NaN  0.000000  0.260870          0.800000  0.818182   \n",
              "       (18, 80]         0.0  0.098039  0.125000          0.391304  0.030303   \n",
              "\n",
              "fare                       \n",
              "class               Third  \n",
              "sex    age                 \n",
              "female (0, 18]   0.318182  \n",
              "       (18, 80]  0.391304  \n",
              "male   (0, 18]   0.178571  \n",
              "       (18, 80]  0.192308  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBpOMuJ8pRfy",
        "colab_type": "text"
      },
      "source": [
        "The result is a four-dimensional aggregation with hierarchical indices (see [Hierarchical Indexing](03.05-Hierarchical-Indexing.ipynb)), shown in a grid demonstrating the relationship between the values."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUr22Sj6pRfz",
        "colab_type": "text"
      },
      "source": [
        "### Additional pivot table options\n",
        "\n",
        "The full call signature of the ``pivot_table`` method of ``DataFrame``s is as follows:\n",
        "\n",
        "```python\n",
        "# call signature as of Pandas 0.18\n",
        "DataFrame.pivot_table(data, values=None, index=None, columns=None,\n",
        "                      aggfunc='mean', fill_value=None, margins=False,\n",
        "                      dropna=True, margins_name='All')\n",
        "```\n",
        "\n",
        "We've already seen examples of the first three arguments; here we'll take a quick look at the remaining ones.\n",
        "Two of the options, ``fill_value`` and ``dropna``, have to do with missing data and are fairly straightforward; we will not show examples of them here.\n",
        "\n",
        "The ``aggfunc`` keyword controls what type of aggregation is applied, which is a mean by default.\n",
        "As in the GroupBy, the aggregation specification can be a string representing one of several common choices (e.g., ``'sum'``, ``'mean'``, ``'count'``, ``'min'``, ``'max'``, etc.) or a function that implements an aggregation (e.g., ``np.sum()``, ``min()``, ``sum()``, etc.).\n",
        "Additionally, it can be specified as a dictionary mapping a column to any of the above desired options:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "69TzoQ2apRfz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "outputId": "5f4882c9-31ac-436a-9acb-0419597e0457"
      },
      "source": [
        "titanic.pivot_table(index='sex', columns='class',\n",
        "                    aggfunc={'survived':sum, 'fare':'mean'})"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"3\" halign=\"left\">fare</th>\n",
              "      <th colspan=\"3\" halign=\"left\">survived</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>106.125798</td>\n",
              "      <td>21.970121</td>\n",
              "      <td>16.118810</td>\n",
              "      <td>91</td>\n",
              "      <td>70</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>67.226127</td>\n",
              "      <td>19.741782</td>\n",
              "      <td>12.661633</td>\n",
              "      <td>45</td>\n",
              "      <td>17</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              fare                       survived             \n",
              "class        First     Second      Third    First Second Third\n",
              "sex                                                           \n",
              "female  106.125798  21.970121  16.118810       91     70    72\n",
              "male     67.226127  19.741782  12.661633       45     17    47"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mMRYseehpRf1",
        "colab_type": "text"
      },
      "source": [
        "Notice also here that we've omitted the ``values`` keyword; when specifying a mapping for ``aggfunc``, this is determined automatically."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "e6kogzUSpRf2",
        "colab_type": "text"
      },
      "source": [
        "At times it's useful to compute totals along each grouping.\n",
        "This can be done via the ``margins`` keyword:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_FQb2rVpRf3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "outputId": "e2852a7d-1b38-4276-e307-6a9ddba3b8da"
      },
      "source": [
        "titanic.pivot_table('survived', index='sex', columns='class', margins=True)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>class</th>\n",
              "      <th>First</th>\n",
              "      <th>Second</th>\n",
              "      <th>Third</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>0.968085</td>\n",
              "      <td>0.921053</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.742038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>0.368852</td>\n",
              "      <td>0.157407</td>\n",
              "      <td>0.135447</td>\n",
              "      <td>0.188908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>0.629630</td>\n",
              "      <td>0.472826</td>\n",
              "      <td>0.242363</td>\n",
              "      <td>0.383838</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "class      First    Second     Third       All\n",
              "sex                                           \n",
              "female  0.968085  0.921053  0.500000  0.742038\n",
              "male    0.368852  0.157407  0.135447  0.188908\n",
              "All     0.629630  0.472826  0.242363  0.383838"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxipojPR3GS3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7d678377-5a8a-464f-a58d-47c422c03a2b"
      },
      "source": [
        "(0.37+.16+.14)/3"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.22333333333333336"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHUjL7GmpRf5",
        "colab_type": "text"
      },
      "source": [
        "Here this automatically gives us information about the class-agnostic survival rate by gender, the gender-agnostic survival rate by class, and the overall survival rate of 38%.\n",
        "The margin label can be specified with the ``margins_name`` keyword, which defaults to ``\"All\"``."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtdjN8X9pRf5",
        "colab_type": "text"
      },
      "source": [
        "## Example: Birthrate Data\n",
        "\n",
        "As a more interesting example, let's take a look at the freely available data on births in the United States, provided by the Centers for Disease Control (CDC).\n",
        "This data can be found at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv\n",
        "(this dataset has been analyzed rather extensively by Andrew Gelman and his group; see, for example, [this blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/)):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEfpm0uDpRf6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "399a831d-1e72-4f2d-bd46-c7ff4e80955b"
      },
      "source": [
        "# shell command to download the data:\n",
        "!curl -O https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100  258k  100  258k    0     0  1196k      0 --:--:-- --:--:-- --:--:-- 1202k\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aXH9oOVgpRf8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "births = pd.read_csv('births.csv')"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pxVZ9paCpRgA",
        "colab_type": "text"
      },
      "source": [
        "Taking a look at the data, we see that it's relatively simple–it contains the number of births grouped by date and gender:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3sDKv0QpRgB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "58dce2cf-3c46-46c1-9398-a7d31c5ad823"
      },
      "source": [
        "births.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>day</th>\n",
              "      <th>gender</th>\n",
              "      <th>births</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1969</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>F</td>\n",
              "      <td>4046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1969</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>M</td>\n",
              "      <td>4440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1969</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>F</td>\n",
              "      <td>4454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1969</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>M</td>\n",
              "      <td>4548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1969</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>F</td>\n",
              "      <td>4548</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   year  month  day gender  births\n",
              "0  1969      1  1.0      F    4046\n",
              "1  1969      1  1.0      M    4440\n",
              "2  1969      1  2.0      F    4454\n",
              "3  1969      1  2.0      M    4548\n",
              "4  1969      1  3.0      F    4548"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CmAq4bmspRgD",
        "colab_type": "text"
      },
      "source": [
        "We can start to understand this data a bit more by using a pivot table.\n",
        "Let's add a decade column, and take a look at male and female births as a function of decade:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w53xI4fvpRgE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "outputId": "868fe660-8e9a-4ba1-8000-1e64ee103c2d"
      },
      "source": [
        "births['decade'] = 10 * (births['year'] // 10)\n",
        "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>gender</th>\n",
              "      <th>F</th>\n",
              "      <th>M</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>decade</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1960</th>\n",
              "      <td>1753634</td>\n",
              "      <td>1846572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1970</th>\n",
              "      <td>16263075</td>\n",
              "      <td>17121550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1980</th>\n",
              "      <td>18310351</td>\n",
              "      <td>19243452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>19479454</td>\n",
              "      <td>20420553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>18229309</td>\n",
              "      <td>19106428</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "gender         F         M\n",
              "decade                    \n",
              "1960     1753634   1846572\n",
              "1970    16263075  17121550\n",
              "1980    18310351  19243452\n",
              "1990    19479454  20420553\n",
              "2000    18229309  19106428"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5HpmXqJcpRgG",
        "colab_type": "text"
      },
      "source": [
        "We immediately see that male births outnumber female births in every decade.\n",
        "To see this trend a bit more clearly, we can use the built-in plotting tools in Pandas to visualize the total number of births by year (see [Introduction to Matplotlib](04.00-Introduction-To-Matplotlib.ipynb) for a discussion of plotting with Matplotlib):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rkHTSSspRgG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "0768219d-5912-40c8-8763-d20fa8a7b184"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set()  # use Seaborn styles\n",
        "births.pivot_table('births', index='year', columns='gender', aggfunc='sum').plot()\n",
        "plt.ylabel('total births per year');"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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X3U/SGXCf+hiyXzhVm5ZgPPAfVKvFQVEKAKqqYMk4SeX6t6n47lnMp7ehjeiLx6w/4zn7BbRhMc1yHaPZypLvaxLEA9N7MapvR/44JxZVhQ//exKj2dos17GFw/skFEVh2bJljBs3zi7n9/Nrd8W2/HwZrfb6E09znON6ybJMQIDXFdvr2+YqRGwNU1WFzNNbMIR0ITh2YJ3S0fXH5oVy3+sUbf6Sy8fWIeWnEDj7MXQ+9T+B2Iuzf25XY4/YVFXl8rHNXDq4GnNRNhpPb7xH3kz7/pPQevk0a2zVJgvvfn6A5IxSHrt9AGMHhtce+9Tv4njl8/38Z0cqj90+wCGlxh2eJF599VU8PDzs1t9QVFSOotSdfKQoynXPlrbHjOumUBTlihESAQFeLjtqQsR2dZa045iLs3Eb9xCFheW1268Z26A7cPPvTvXOf5Dx6RO4Df+dw0pPu8LPrSH2is10agvGvV8jB0TiNvb3aKMGYdXoKKkGqm27ni2xmcxWlixPICmthPtn9CSmk3edYyL8PYgfHsmK3Rfo6OvB+IFh1/O2AJBlqd6b69rXr/sKjbB48WLS0tJ4991323yT0ty5M4mPn4zV+stj47p1qxkxIo4ffvjOiZEJjmRK2IDk6Ys2Kq7Rx+oi4/Cc+yoav05U7/iUqm2foJoc217dFlgLLmLc/y2aTjXNSrpuNyBpdM1+HZPZyns/1CSI+6b35IaY+ksHzRjemb5d/Ph261lSMkqbPY7fctgn9dtvv82pU6f48MMP0ev1jrqsS/PzC+DgwX21369bt5ru3V1/OUOheVgLL2LNSUIfOxFJbtpDvdzOD/cZz6IfOBtL6n4q/vsi1vzzzRxp26WaKqna8iGSe3vcx8y3Wx0tk9nK+z8kcOZiTYK4Wm05WZKYP7MXfh3c+GjFKUouG+0SU+317Hr2/zl79iyffPIJ+fn53HbbbcTHx/OHP/wBgPj4ePLy8gBYs2YNo0aNYsOGDSxZsoRRo0Zx7tw5R4ToFNOmzWDdujUAZGVlUl1dTZcuouBfW2FK2Ag6N3TRo6/rPJIsYxgYj/vMhaBYqVz5f5hT9jRTlG2XqqpU7/oCtbwIt/EP15m/0ty+2pjM6Ysl3Dvt6gniZx5uOv44J5Yqk4WPVpzCYrVfU7hD+iS6detGcnJyva+tXLmy9usZM2Y4ZPirOWVPzVT5RpAkyaZCa7oeo2xuG+7fP44ff1xOWVkZGzasZcqUaSQnJzUqLqFlUsqLsaQeRBczAUnv0Szn1AZ3x/OmV6ja/AHVu/6B3D4QTXC3Zjl3S6BUlVGZeg7Vq0uzdOiaT2/Dcv4Q+sE3o7Xjz7G4rJp9iXlMiAtnRB/bq1OHBbTjvmk9+XhlIt9uPctdk3rYJT6bniQUxfkdtq2RJMG4cRPZunUTW7ZsZOLEqw+BFFoPc+IWQEEf07xlNiSDJ+4T/oDUzo+qzR+gVJQ06/ldWfX2peR++xrVm95DqSq7rnNZC9Mw7luGJrwP+r5TmynC+m0/loWKysS4xndCD+4ZxOTB4Ww7mmW3/olrPklYrVb69+/P4cOHW01fgq778EaPBLHX6KYpU6bz4IP30Ldvfzp08G728wuuRzVXYzqzA23kIGSvgGY/v+TWDvdJC6hc8SpVm97HY+azSNrW8W+3IZbsM1gzT+Ee1Y+qiyexLl+E2+j70Xbq2+hzqaaq//VDeOE21n79EFDTF7HzeDb9uvrj7+3epHPMHdOFAG93gv2a54n0t6757jUaDZ07d6akpO3ckThSaGgY8+c/wj33PODsUAQHMafsAVMl+j6T7XYNjW8YbmPnoxScp3r3V616TQpVVTEeXI7k6UPQ3KfxmPMiknt7qja8Q/Xuf6FabO/Yre2HuFyI2/iHkd3sOx9k/+k8yqvMTIwLb/I5NLLMuAFhtPewz42ATX0SM2fO5KGHHmLevHkEB9edtDNsmFhy83rFx89xdgiCg6iqivnMDmT/CDSBXex6LV1kHMqAGzEdXYXZP6LZm7ZchSXtGEp+KoaR9yDrDGh8w/GY9QLGw//FnLABa9Zp3MY9iCYg8prnMp/ZjuX8QfSD5qIN7m7XuFVVZcvhTMIC2tGjk+u2ItiUJJYtWwbA+++/X2e7JEls3bq1+aNqAxoqFf788y85NhDBoZSCCyjFGRhG3O2Q6+kHzsJamI5x37+RfUPRduzpkOs6iqoomA4tR+oQjK7HyNrtklaP29Db0Ib3oXrHZ1SueA39wHj0/aYjyZp6z1XTD/FvNGEx6PtNs3vsyemlZBaUc8/UaIfMnG4qm5LEtm3b7B2HILQJ5jM7QGtA19Ux6xxLkoz7uAepXPEK1Vv+jsfsF5G9/B1ybUewnNuHUpKN24RH6v3w14b2wnPuq1Tv/hemw//FdGxNzYgRVKhtgVNr/lOsSO4dcBv7e4esK775cAbt3HUM7RVk92tdD7GehCA4iGqqwpx6AF3XIUj6pnVSNoWkd8d90qNU/PhyTUd2/HNI2pa1znJ9VKsZ4+H/IvtHoI1seMa6ZPDEffzDmKMGYc09+7+NP9+5S7/cxUsy2m7DkN3b2zdwoKC0iuPnCpk2NAK9rv4nG1dhU5IoLy/n/fff59ChQ5SUlNTpBNuxY4e9YhOEVsV8bj9YjOiixzj82rJ3MO7jH6Jqw7tU7/wCt3EPunQThy3MZ3bUTHQbda9Nd/66yDh0V0kmjrTtaCYSklMWEWosm56pXnrpJU6fPs0jjzxCaWkpixYtIiQkhHvuucfO4TWf1jC6ozW8h7bMnLQD2S8c2YYOVHvQduqLftBNWFL3Yzr4fYv+e1LN1ZiOrkITEo0mtLezw2mUapOFXSdyGNgjAN/2bs4O55psepLYs2cP69atw8fHB41Gw4QJE4iNjeWhhx5qEYlCq9VTUVGGp2f7Fnv3pKoqFRVlaFv5ePfWylpwEaUwDcPw3zn1b1DfbzpqeRGmE+tQVQXDkFtb5L8J08lNqNWXMQye2+Li33cqlyqj5bqGvTqSTUlCURS8vGrGC3t4eHD58mUCAgJIS0uza3DNxccngJKSAsrLmz4jUZZlp88812r1+Pg0/+Qrwf7MZ3aARo+um3OHjEuShGHEPJBlzAkbQLFiGHZHi/qgVaovYzqxHm3nAWiCWlatM1VV2XIkk4hgL7qE2r/voznYlCSio6M5dOgQw4YNIy4ujpdeeglPT086d+5s5/Cah0ajxd/f9poo9XHlGvqCa6vpsN6PtsuQZqvTdD0kScJww10gaTCf2lSTKIbf5ZARPc3BdHwtmKvRx93k7FAaLfFiMTlFlTwwo2eLScw2/VW89tprhIbWdLA8//zzuLm5UVZWxl/+8he7BicIrYE59UDNh1rP66v22pwkScIw7HZ0faZgPr0N4+5/oaquX6NNKS/GnLgFbfcb0Pi6fqfvb205nEl7Tz2Dol172Ouv2fQkER7+S9uZn58f//d//2e3gAShtTEn7UT2CUO2cYa10Wwlt6gCew+MlCSppk9C1tTcnSsKhlH3uPQThenoClBVDANnOTuURssuKCchtYgbh3dG5wJLIdvKpiShqirff/89a9asoaSkhNWrV3Po0CEKCgqYNs3+MxMFoaWyFqahFFzAcMOdNjcvfLk+iUNn8pg5PJIZN0SgseMqjpIkoR80F2QNpqOrUFUFt1H3IbnYypGqxYj53H7MybvR9R5vl8KI9rZ693k0cssY9vprNv0lLFmyhOXLl3PrrbeSk5MDQHBwMJ999pldgxOEls6ctBM0OnTdbrBp/7ySSg6eySPAx4OVuy/wl38fo+hStV1jlCQJQ9ycmtXtUnZTveNTVBdZHsBakk313m8o//oxjLu+QPbpiL7/TGeH1WhVRgtbD6UzuGcgHdq1rImMNj1J/Pjjj/z444/4+vry0ksvARAWFkZGRoY9YxOEFk01GzGf3Ys2ajCSwdOmYzYeSEcjy/zlTyPZfSSDf21K5sV/HOSeqdHERQfaNV7DwHiQZUyHfqAaFbcx8xusc2RPqtWM5cIRzGe2Y81JBlmDNjIOXc+xaEJ6tJgO359dyClj5e4LVBmtTGghw15/zaYkYbVa8fSs+SP/+RdUUVGBh4fzR2oIgquy/K/DWtdzjE37l5Yb2X0yhxGxIfi2d2NYTDBdQtvzyarT/H3FKUb2CeGOCd0x6O33wW3oPxMkCdPB5VSrak0dIwclClWxYDq8AnPSTtTqy0heAegH34KuxwiHlMpoToqqkpBaxMYD6SRnlOJu0HDX1GgiQ1rW+wAbk8To0aN54403eO6554CaPoolS5YwduxYuwYnCC2ZKWkHsk9Hm8fybz6cgVVRmTykU+22QB8PFt41gJW7L7BuXxpnMy/x4I29iQi23zoHhn4zkCQZ44H/UK0qNSU8ZPuWeatZx+FLLCm70XYegK7XODShvVy6E70+ZovCvsRcNh5MJ6eoEt/2Bm4b15WRfTvSKcynRQ6jt+k3v3DhQp555hkGDhyIxWKhf//+DB8+nMWLF9s7PkFokaxFGSj55zEMu92m5pHKags7jmUxKDqQIJ+6T+hajcxNo7vQK8KHT9ec5v++OsztE7rbtQNU33caSBLG/d/VPFGMf8iuicJ0dCWWlN3oB8RjiJttt+vYS3mVmR3Hsth6JJNLFSY6Bbbj9zN7ERcdiFbTshLdb9n0W2/Xrh0ffvghhYWFZGdnExISQkCA7aMLSkpKePrpp0lPT0ev1xMREcErr7yCr69vnf2qqqpYuHAhiYmJaDQannnmGfG0IrRI5qQdoNGi62bbMrnbj2VSZbQydUhEg/v07OzLy/cN5rM1Z/hqYzIms5XJgzs1uP/10veZCsgY9y+jestHuI1/GEnT/InCnLIb05EVaLsPR9/ChrZezC1j65FMDpzOx2JViInyZf7gTvSM8GlxfScNsek3/vrrrzN79mx69uyJv3/ja9FLksQDDzzAkCFDAFi8eDFvvfUWr7/+ep39Pv/8c9q1a8fmzZu5ePEid955J5s2bartDxGElkC1/K/DOnIQklu7a+5vtljZfDiTmEjfazYjeXno+dNNsSxdlch3286hqOpVE8v10veZDLKMce83VG/9O27jH2nW81syE6ne+QWa0F64jby3RXywWqwKh5Ly2XYkk9TsMgw6DSP7hDBuQCihAdf+fbc0Ntduuv/++/H19eXGG2/kxhtvvGIZ06vx9vauTRAA/fr1q13t7tfWr1/Pm2++CUDnzp2JiYlh165dTJ061eZrCYKzmRO3gakKXa9xNu2/52QuZRUmpg217cNeq5F5ML438urTfL89FUVRmT6s83VEfHX6mImAhHHv11Rv+RD1tmea5bzWogyqNr+P7B2C+8Q/2uUppTmVXDay41gWO09kU1ZhIsjHndsndGN4TAgebq4d+/WQVBvrBVutVnbt2sXq1avZvn07ffv2ZdasWUycOLFRd/qKonDfffcxbtw45s2bV+e1/v37s3Xr1tpmqJdeeomIiAjuvffeRrwlQXAexVhJ+oePYAiJIuT2F665v9Wq8NDirbT31PPWglGNupO2WhXeWXaMnccyuWtqNLdO6HE9oV/TpcMbKNr4KYawHnhGD8UttDv64CjkJlQmtpQVkfXls6BC6L1voG3v2qvlJV0s5vmP9mC2KsT1DGLG8Cj6dQ9All3/yed62Zz+NBoNY8eOZezYsZw9e5YnnniCZ599lpdffplp06axYMECgoKuXY/k1VdfxcPDg7vuuuu6Am9IUVE5itL8dfJducCfiK1p7BGb8chKlKrLSH1n2XTug2fyyC2q5KZRXSgsLG90bL+b2A2TyczX65Mov2zkxhF2XKsiYjhuoxUsx1dTvOWfNdtkDbJ/BJrALmiCuqIJ7ILUzu+qyU41VVG5+nWU6ko8Zi6kxGiAZvo92ON3WlxWzav/PIx3OwOP39qXwP8NLCgqKr/GkfaPrTnIsoSfX8PNZDYnifLyctavX8+qVatISUlh0qRJvPjii3Ts2JF//OMfPPDAA6xevfqq51i8eDFpaWl8/PHHyPVM++/YsSNZWVm1TxI5OTl1mqkEwZWp1eWYEtaj7TwQjQ0LC6mqyrp9aQT7etC/e9PupGVZ4v7pvZAliRW7L6CoKvOB3xwAACAASURBVPEjIu3Wtq/rMZKOI6aRl5aBNT8VJS8Va34q5jM7MZ/aDIDk5lWzuJJvOBrfsJqvvTsiafWoioWqLR+iFGfhPuUxNP72609pDiazlQ/+exKj2cpTt/WrTRBtiU1JYsGCBfz0008MGjSI22+/nQkTJqDX//KIuXDhQgYOHHjVc7z99tucOnWKpUuX1jn216ZMmcJ3331HbGwsFy9e5OTJk/ztb39rxNsRBOcxnVgHZiP6uDk27Z94oZj0/HLunRaNfB0f6rIsce+0mtLTq/ZcRFFh9kj7JQoA2cMbufNA6Fzz715VLCjFmVjzzqEUpmMtzsB8ejtmq6nmAElC7hACOkNNLatR96INj7VbfM1BVVW+3JBEWu5l/nhTbKvslLaFTUmib9++/PnPf25w2Kssy+zdu7fB48+ePcsnn3xC586due2224Cash4ffvgh8fHxLF26lKCgIO6//36effZZJk6ciCzLvPLKK7Rr1zZ/MULLolSWYjq1BW3XoTaXsF63Pw0fLwPDets+CKQhsixxz7RoZBnW7L2IViNx43DHLZMqyVo0/p3R+Heu3aYqCmpZPtbiDJTiDJSiDJTSHPSDb0Ef3fxl089lXWLl3jSG9Qxoljv+DQfT2Z+Yx+xRUfTv1vIKCjYXm5LE/ffff8193N3dG3ytW7duJCcn1/vaypUra7/28PDgvffesyUkQXAppqOraxbvsXEiWGr2JZLSS7ltXNdmm2wlSxLzpkRjsaqs+OkCgT7uDO11/QmoqSRZRvIORvYOhqhBdr2WVVH4fO0Z8oorWf3TeW6ICWbG8M4Eejf8uXQ1CalFLN+eSlx0IDOGuXaTmL213nFbguAgyuUCzEk70PUYidzetiJ86/en4+mmZVS/js0aiyxJ3D0lmsLSKv6xNgn/Du50De3QrNdwRT8l5JBXXMkf5vYl5WIxO45nsS8xtyZZ3NCZgEYki5yiCj5ZlUh4YDvun9ZyVpCzl5Y9X1wQXIDxyCqQJPQDbrRp/5yiCo6lFDB+YBhu+ua/T9NpZf4wJxZfLwPv/5BAYWlVs1/DlRhNVlbuvkDX0A5MHhrB7RO6sfihYYztH8q+xDyeW7qfL9efsennUFlt4f0fTqLVSPzxpli7FlNsKa6ZJFRVJSMjA6vV6oh4BKFFUUpzsJzdja7XeOR2vtc+gJolLDUamXEDw+wWl5eHnkdv7oPVqrJkeQKV1Ra7XcvZNh/O4FK5ibljutTe9Xu3M3DHxO4sfmgYY/qFsvdULguX7ue95Qn8sDOVPSdzuJBTRpXxl5+Loqh8siqRgtIqHpkVg3+HpjVVtTbXvI2RJImZM2dy9OhRR8QjCC2K8fCPoDWg7zfdpv2rjBb2JuYypGcg7T0aPwmtMUL8PPnD7Bje/s8JPl51ikfn9rHrKnfOcLnSxPoDafTr6k/3cO8rXvfxMnDnpO5MHdqJ9QfSOX2xmJPni7D+ai6Vj5eBED8PJEki8UIxv5vcgx6dfBz5NlyaTc+6PXv25MKFC3TpYtsavYLQFlgL07CcP4i+/0yb1zvYl5iL0WRl7AD7PUX8Ws/Ovvxucg++XJ/Et1vOceek7g65rqOs3ZdGtcnKTaOjrrqfb3s37pxY894tVoWC0ipyiirJKaqo/X9+SRVThnRqccuL2ptNSWLw4MHMnz+f2bNnExwcXKcjZ+7cuXYLThBcmfHwf0Hvgb7PFJv2V1WVbUez6BzsRVRHxy0+M6pvR3KLKtlwMJ1gPw/G27GZy5EKS6vYdjST4bEhjZrDoNXIhPh5EuLnCbTdoa22silJHD16lNDQUA4ePFhnuyRJIkkIbZI17xzW9BPoB8+1eWnSlIxSsgsruHdatJ2ju9LcMV3ILa7k31tSCPRxJzbKz+ExNLcff7qAJEnMsmcpEsG2JPHVV1/ZOw5BaDFUxYrx4PdI7u3R955o83Fbj2bh6aZlSM9r1zhrbrIs8fsbe/Hm10f5aMUpHr+1X4seGpuRX87+xFymDOmEb3s3Z4fTqtnci1VSUsKKFSv47LPPAMjLyyM3N9dugQmCK1IVC9Xbl2LNSUY/6CYkncGm40ouGzmWUsCIPiHodc4ZVumm17Jgbh+8PHQs/uYoO45lYWMRaJfzw85U3A1aprXxiW6OYFOSOHjwIFOmTGH16tV8+OGHAKSlpfHSSy/ZMzZBcCmq1UL1lo+wpB7AMKRxpSV+OpGNVVEZ4+ROUd/2brxwzyB6dfblXxuT+WJ9EmZLyxrenpRWQkJqEdNviMDTTefscFo9m5LE66+/zrvvvsvnn3+OVlvTQtW3b18SEhLsGpwguArVaqZq8wdYLh7BMOyOmjWgbWSxKuw4nkVMlO8V61c7g6ebjkfn9mHGDZ3ZnZDDG18fpehStbPDsomqqny/IxUfLwPjHTRCrK2zKUlkZWUxbNgwgNqRTTqdTkywE9oE1WKiauMSrOnHMYyYhz52UqOOP362kNJyE+P6u86HmixLzBkVxR/nxJJbXMnLXx7iTFqJs8O6piPJBVzIKWPWyEinNdu1NTYliS5duvDTTz/V2bZ37166d29dY64F4bdUs5GqDe9gzUzEbdR96G1ckvTXth3NxK+9G326uN6IogHdA/jz3XF4eej427fH2Xgw3WX7KcwWhR92nSfU35PhMSHODqfNsGl007PPPsuDDz7ImDFjqK6u5oUXXmDbtm38/e9/t3d8guA0qqmqJkHkncVtzAPoug9v9DmyCytISi/lptFRLrvUZYifJ4vmxfGPtWf4bts5LuSUcf/0Xui0zp+dnVdSyanzxSReKOZMeglGk5UFN/Vx2Z9la2RTkujXrx+rVq1i1apV3HTTTYSEhLB8+XKCg51XhlgQrofx6CouJm4GD19kLz+kdv41//fyR27nj+TWjqqtH6Hkn8dt3EPoujRthcTtx7LQaiRG9mneaq/Nzd2g5ZHZMazbn8YPO89jNFn5w5zYZitjbqsqo4UzaSUkXijm1IUiCkpr+kr8O7gxrHcwA7r5E9MK5ni0JDaXoAwKCuKBBx6gpKQEHx+fNl8+V2i5lMsFmI6uwhAUgUXriXIpFyXzFFhMdXeUNbhNeARdZFyTrlNtsrD3VA5x0YG097RvnabmIEkS04d1xsOg5atNKXy8MpGH4ns7LFGkZJTyzvcnMJqsGPQaenbyYdKgTsRE+RLo7S4+c5zEpiRRVlbGq6++yoYNG7BYLGi1WqZMmcLzzz+Pt/eVRbUEwZUZD/0AkkzQ3GcoMdZ8eKuqimosR71ciHK5ELW8EE1QNzRBXZt8nf2JeVQZrYxrYaNwxg4Iw6qo/HvLWZauPs2DN/aye2HA7MIK3v8hAZ92BuZN7kHXsA4Of4oR6mfTb2HhwoUYjUZWrFjB0aNHWbFiBSaTieeee87e8bkEa945KpIOODsMoRlYCy5gObcffZ/JaNv/0mwhSRKymxeagEh0UYPQ95l6XQmipk5TJp0C29HFgXWamsuEuHBuHdeVw0n5fL7mDIpiv87skstG3vnPcTQamcdu6Ut0hI9IEC7EpieJ/fv3s2fPHtzcaqa/d+nShTfffJORI0faNThXoKoq1bu+pKqiEM/ffYCkEYv5tVSqqmLc/y2Sm1ej5jk0xdnMS2QWVHDP1OgW20wyeXAnrIrK8h2pyLLEfdN6NnuHcZXRwrvfn6C82sKzdwxo1ApygmPYlK6joqLIysqqsy07O5vIyNZfWEspTEMpyUQ1VWPNTXF2OMJ1sKYfrymnETcbSW/fD6Ptx7JwNzinTlNzmjY0gtkjI9l7KpcvNyShNOPwWItV4YP/niS7sII/zI4hItir2c4tNB+bbouHDRvGfffdR3x8PMHBweTm5rJq1Sri4+NZvnx57X4NVYRdvHgxGzduJCsri9WrV9c7v6KgoIAXXniBzMxMLBYLDz30EPHx8U18W83HnLIbZC2SJGFJP4E2tJezQxKaQFWsGA/8B7lDMLroUXa9VsllI4eT8hk7ILRVLH85c3gkVkVl1Z6LaGWJx+9qWkf+rymqyj/WneFMWgn3T+9JTKQYseSqbEoSx44do1OnThw7dqx2W3h4OEePHq1dse5qZcPHjx/PvHnzuPPOOxu8xptvvklMTAwfffQRxcXFzJkzh8GDBxMS4rxJM6rVguXcfrSd+6PDjDH9BAy73WnxCE1nTtqJUpqD26QFSLJ9mwxX7bkA1LTrtxbxIyKxWFXW7U/DaD1EmJ8H7Tx0eHno8fr5/+463PQam5rXftiZyv7EPGaPimJ4rJgY58ocUio8Lu7adx5JSUncfffdAPj6+hIdHc369eu57777ruva18OScQLVWI6u+wg8lDKqzn+OUpaP3D7QaTEJjaeaqjAdWYEmpAfaiP52vVZ2YQW7TmQzfkAYga2ofV2SpP9NCIRNBzPYZ1Hq3U+rkQn0cScyxIuojh2ICmlPWKBnndFRW49ksn5/OmP6hzJDVHF1eS7TC9u7d2/WrVtHbGwsmZmZHDt2jLAw5w4dtKTsQXLvgCYsBg99NUWbPseSfgJ9jO1rCAjOZzqxDrWqDMPk/2f3TuTlO1Jx02uYMbyzXa/jDJIkMWdUF34/py8ZWaVcrjJzudLE5cqa/5dXmrlcaSa7qIIT54rYc7JmKQG9ViYi2IvIkPa0c9fx467z9Ovqz10Tu7fYTv22xGWSxLPPPsvrr79OfHw8HTt2ZNiwYWg0jW/P9fOzfRnDq7FWXCIt/QQdBk3HL6hmLojOryNy7ikCxs5plms0p4AA1+30c2ZslrIiMk5uxLP3CIJ6973i9eaMLfF8EcfPFTJvWk+6RFx/G7sr/047hflc9XVVVckrriQlvYTk9BJS0krYfiwLs0WhR4QPz98/BDe9fT5+XPnn5sqxNcRlkoSvry9vvfVW7ffz58+na9fGj1MvKipvljHdplNbQbFiDh9EQcHlml9ux1iqEreSn11o82IzjhAQ4EVBwWVnh1EvZ8dWteMrVEWBPvFXxNGcsamqytIfE/DxMjCsZ+B1n9fZP7ersTU2DdAzrAM9wzrADZ2xWBVyiysJ9vXg8qUq7PHuWsPPzdFkWbrqzbXLzFgpKSnBYrEAsG/fPlJSUpgxY4bT4jGn7Eb2j0Dj+0vno7ZTX1AsWLNOOy2utkI1VVG9+1+Yzx9q8jmsRRlYUnaji5mA7GXfBe+PJBdwPruMWSMiMYgS1vXSamTCAtqJiXItjE2/rXPnzlFYWAhARUUF7733Hh988AFVVVU2XeS1115j1KhR5Obmcu+99zJ9+nSg5mnh5MmTACQkJDBt2jSmTJnCe++9x8cff4y7u3M6/qzFGSiFaei6j6izXRPcHXRuWNJPOCWutkKpvkzl2r9gPr2N6i0fUr33G1SrpdHnMR74DgweGPrPtEOUv7BYFZbvTK0pYS1G6gitjE3NTY8//jjvvvsu/v7+LF68mAsXLmAwGHjhhRf461//es3jFy1axKJFi67Y/umnn9Z+PXr0aEaPtn05SHsyp+wBSYP2N5U/JY0WbWhvLBkJqKoqOt3sQCkvomrdWyiXC3Gb+EesOcmYT23GWnAB9wl/QPa8els4gGquxnRiHdbMUxiG3o5k8LRrzDuPZ5NfUsX/u1mUsBZaH5uSRFZWFlFRUaiqyubNm1m7di1ubm6MHz/e3vE5nKpYsZzdi7ZTH2T3K2vuaDv1xXLxCEpxJhq/1jMO3hUopTlUrv0rqqkK92lPog3pgS4yDk1QV6p3/oPKH17AbfzDDU5oVK0WzGd2YDq2CrWqDG1kHLrejV8kqDGqjBZW7blAdCdvYkUJa6EVsilJGAwGysvLSU1NJSQkBF9fXywWC0aj0d7xOZw181TNB8xvmpp+punUBwBL+gmRJJqRteAiVev/BpKEx8xn0fj/Mn5e12UIsm841Zs/oGrdX9HH3YS+3zQkqaa1VFUVLOf2Yzz8I+rlAjQhPTBMWnBdBfpstf5AOpcrzdw8tqt4shRaJZuSxIwZM7j77rupqKjgrrvuAuD06dNOn8dgD+aUPUiGdjWd1PWQPbyR/SOwpp+A/s7rWG9NLNlnqNq4BMngicf0p5A7XLmYlcanIx6zX6B65z8wHVqONe8s7mN/jzXvLMaDP6AUZyD7ReA29Qk0YTEO+cAuuWxk08F0BvcMJDKk5VV6FQRb2JQknnvuOXbv3o1Wq2Xo0KFAzcSahQsX2jU4R1ONFVguHkXXc8xVq71qO/XFdGw1anU5klvzzMtoq8wXj1C95SPkDkG4T3vyqn0Oks4Nt/EPYw7uhnHft5R/8xhYTEjtg2qaoaIG1T5dOMLK3eexKipzRndx2DUFwdFsnicxYkTd5pfY2NhmD8bZzKkHQLFcMarpt7Sd+mI6ugpL5il0XYc6KLrWRblciPncfkyHf0AOiMJjymM2JVxJktDHTEQTEInx8H/RRg5CFz3S7vWYfiursIKfEnKYMDC8VZXfEITfsulfVkZGBu+++y5nzpyhsrKyzms7duywR1xOYU7Zg+wTiux/9XoyckAkkpsXlvQTIknYSFUUlPxULOknsKQfRynOBEAT3gf3CX9o9ORETVBXPKY/bY9Qr0lVVb7ffg43vZaZrbD8hiD8mk1J4sknnyQ8PJxnnnnGaXMX7E0pzUHJT8Uw5NZrtmdLkowmPBZregKqoiDZeWnHlkq1mCg/vYeqU/trflbGcpDkmo7lobeh7dQP2fvK/gdXpqgq32xKISG1iFvHdaWdu87ZIQmCXdmUJM6ePcuyZcuQW/GHYc3cCAltt2E27a/t1BfL2b0oBecdMoqmJare9QXl5/aBwRNteB+0Ef3QhsXYfd6CvSiqylcbk9l5PJupQzoxaZAY3Sa0fjYliUGDBnH69GliYmLsHY9TqIqC+exeNGGxyB7eNh2jDYsBSa4ZCiuSxBWU8iIsqQdoP3AKSv9bWvzTlqKqfLk+id0JOUwfFsGcUVFiyKvQJjSYJJYsWVL7dWhoKA888AATJ07E39+/zn6PPvqo/aJzEGtOEmpFMbqht9p8jGTwRBPUFUv6CQyDbrJjdC2T+fQ2QKXD0HhKzS08QSgqX6w7w55Tudw4vDPxIyJFghDajAaTRG5ubp3vx44di8ViuWJ7q6DRognt3egFaTSd+mI6+D1KRYlN5SLaCtVixHRmB9qIAei8A8EFK1/ayqoofL72DPsT85g1IpIbR7T+dd0F4dcaTBJvvPGGI+NwKm1wd7TTn2r8cf9LEpaMBPTRrlF3yhWYz+4DYwW62EnODuW6WBWFT1ef5uCZfOaMimLGDZ2dHZIgOJxN7QCDBw+ud/uwYbZ18rZWsk8oUju/mtnXAlAzPNR8ahOyX0RN1VwXpaoqOYUVlFw2UlFtxmxRUNVf1iGxWBU+WVWTIG4e00UkCKHNsqnj2mw217tNUepf57atkCQJbXgfzOf2oVrNSBoxHNKalYhSko3bmPku3W7/6ZrT7E/Mq7NNliT0Ohm9ToMEXKowceu4rkwe3Mk5QQqCC7hqkrjjjjuQJAmTycSdd95Z57Xc3Fz697fvovItgbZTX8xntmPNSUEb1tvZ4Tid6eQmJPf2aLvU//TpCk6cK2R/Yh6Th0YQ5O2GyaxgMlsxmq01X1tqvo6N9GNYTMuaxyEIze2qSeLmm29GVVVOnjzJ3Llza7dLkoSfn19tHae2TBPaEzRaLBkJbT5JKKW5WDMS0A+c5bJPVUaTla83pRDi58GDs/tQWlLh7JAEwaVdNUnMnj0bq9XKjh07mD59Onq93lFxtRiS1oAmIAol/7yzQ3E606nNIGvR9Rzr7FAatHLPBYrKqnn2zgHotC17aK4gOMI1/5VoNBoOHDiAVuvYAmquJCmthB1HMhp8XfYNw1qSWafjs61RjRWYU3aj7ToE2aODs8OpV3reZTYdzGBU3xC6h9s2aVIQ2jqbbqVmzZrFsmXL7B2LS/p5pu37/zlOtan+dZZl3zAwVaFWFDs4OtdhTt4FFiP6GNcc9qqoKv/amIynu5a5Y8QMeUGwlU2PBwkJCXz99dd8/vnnBAcH1xm18s0339gtOFdw+mIx+aVVAJw8X8yg6MAr9pF9axZfUoozkdu1vSUsVUXBlLgVTUiPOivKuZKdx7I4n13G/Bm9RFE+QWgEm5LELbfcwi233NLkiyxevJiNGzeSlZXF6tWr6d79yvHzRUVFLFy4kJycHCwWC0OGDGHRokVOb+bafjQLLw8dkiRxJDm/3iSh8QkFwFqc2eCKdq2ZJe0Y6uVCdENvd3Yo9SotN7J8Zyo9I3wY2jvI2eEIQoti0yfw7Nmzr+si48ePZ968eVcMo/21jz/+mC5durB06VLMZjN33HEHmzZtYtq0add17etRXFbN8XOFTBnSCQWJHUczMVus6LSaOvtJBk8kT9/aNRLaGvOpTUhe/o0ua+Io3249i9miMm9yD5eeuyEIrqjBJLFixQpmzZoFwPLlyxs8wa+HxjYkLi7umvtIkkRFRQWKomAymTCbzQQFOfeub9eJbFBhdL9QjFbYuD+NUxeK6d8t4Ip9Zd+wNpkkrIVpWHOSMQy9zSUrvSakFnHwTD6zRkYS5Ovh7HAEocVpMEmsXbu2NkmsXLmy3n0kSbIpSdjikUce4U9/+hMjRoygqqqKO++8k4EDBzbLuZvCYlXYdSKb3lG+BHq74+3jiYdBy+GkgnqThMY3DFNWIqpicfhSms5kOrUJtAZ0PUY2+7mLy6rx8tA3eaiq0Wzl603JhPh5MHWIa/aVCIKra/DT7NNPP639+quvvrJ7IBs2bKBHjx7885//pKKigvnz57NhwwamTJnSqPP4+V17nWRb7E3IprTcxB9v7kpAgBcAQ2NDOHAqB28fzys+uC5HdKXgxDq85cvoAxxfxuHnGB3JUl7K5dQDtO8/Ef+whmcmNyW2nUczeWfZUSI7tuf5e4fg34R1pL9ck0jhpWreeGQ4HUPqH5brjJ+brURsTSNia1423/KWlZWxY8cO8vPzCQwMZMyYMbRv377ZAvn66695/fXXkWUZLy8vxo0bx4EDBxqdJIqKylGU65+vsGrnOXzbG+gc4ElBwWUCAryIifBh2+EMfjqSTmxU3VFMVl3NOhuF55LR4diy4QEBXhQ4oRy38fgGsFqwRo1u8PpNiW3HsSy+2phMRLAXmfnlPPr2Dv44O5auYbbPvzh1oYgVO1MZ0SeEoPaGemNw1s/NFiK2phGxNZ4sS1e9ubbpOX7fvn2MGzeOr776ipMnT/L1118zbtw49u3b12yBhoWFsWvXLgBMJhP79u2jW7duzXb+xsgrriTxYgmj+3ZEln/p6Owd6YNBr+FIcv4Vx8jeISDJbapfwnLhMHJAVLOuU71+fxr/2phMbBc/nr1zAM/Pi8NNp+Evy47y04nsax5/qcLEp6sTefu7E/h3cOOWsWJOhCBcD5ueJF599VVeeeWVOiON1q9fz8svv8yGDRuuefxrr73Gpk2bKCws5N5778Xb25u1a9cyf/58FixYQGxsLM899xwvvvgiM2fOxGq1MmTIkOsadns9dhzPQiNLjOzbsc52nVZDv67+HE0p5HeTFTS/6qiVNDpk72CUkixHh+sUyuUClIIL6Ac3z+9IVVX+u+s8a/elMbhnIA/M6IVWIxPq78miu+P4eOUpvlifRGZBBbeM61LnZw81k+V2Hs/mhx2pGM1WZt7QmenDItDrNA1cURAEW9iUJPLz85k8eXKdbRMnTuTPf/6zTRdZtGgRixYtumL7r/s9OnXqxBdffGHT+ezJZLayOyGH/t388W5nuOL1gd0DOHA6j5T0Unp29q3zmuwThrXggqNCdSrLhSMA6KKuPXLtWhRV5d+bU9h2NItRfTsyb3KPOk9w7dx1PHZLX77bdo7NhzPILiznwfiY2klxabmX+dfGZC7klBHdyZvfTe5BiJ/ndcclCIKNzU3x8fFXzKxetmxZ7ein1uRwcj4V1RbG9g+t9/XYKD/0WpnDKQVXvCb7hqFeLkA1Vdk7TKczXziM7BeB3P7KyYWNYVUUPl9zmm1Hs5gyuBN3T6mbIH6mkWXumNCde6dGk5Reymv/Okxq9iWWbTnLK/88RNGlKubP7MVTt/cXCUIQmlGDTxI/ryUBoCgK3377LZ999hlBQUHk5eVRVFRE376tb3bx9mNZBPl6EB1Rf+ezQa8hNsqPo8kF3DmxO/KvJmdpfMMBUEqy0AS13rZwpbwYJe8c+kE3Xdd5zBYrH69M5NjZQmaPimLGsIhrTnYb2bcjIX6efPDjSf7vX0eQgNH9Q7lpdBSebqLchiA0twaTxM0331zne2f1DzhSet5lUrPKuG1c16t+WA2MDuBISgHnMi/VqSb6cw0na3Fmq04Slov/a2qKbFpTU1mFiYNn8th1IofMgnLumNCNCXHhNh/fNawDL9wdx/r96QyNCaJLR9esOisIrUGDSeJ6S3G0RDuOZaHTytwQG3LV/fp28UerkTiSXFAnSUhefqA1tPoRTpYLh5F9wmpGdNnIaLJy9GwB+xPzSLxQjKKqhAe24+FZMfXWw7oW3/Zu3DnJddfQFoTWou1MDb6GKqOFfafzGBwdeM0qoe4GLb07+3IkJZ/bxv/y1CFJMrJvaKtOEkplKdacFPQD46+9r6JyJCmPDXsvcCylEKPZim97A1OGdGJo7yDCAppn4qMgCPYjksT/7E/MxWiyMmZA/R3WvxUXHciJ1CIu5FwmquMvkwo1vmFYLhxFVdVWWUzOcvEooKK1oanp680p7DiWhYdBy9DeQQztFUS3cO86/TiCILg2kSSoGaO//VgWnYLaERVi2yzyft380cg15cN/nSRk33DUpF2oVZeQPFrf6meWC4eROwQj+1w9mRZequKnE9lMHNyJuaOixFKhgtBCiX+5QGpWGZkFFYzpH2rz3b+nm47oCB+OJBfUWbb01wsQtTZK9WWs2UloowZd8+e08UDNHQ4WgAAAHFpJREFUcq+3T4oWCUIQWrAGnySWLFli0wkeffTRZgvGWcxWhcgQL4b2alxp8rgeAfxzQzIZ+eV0Cqop3FUnSYTFNHuszmS5eBRU5ZpNTWUVJnYlZDOsdzABPu4uWa9GEATbNJgkcnNzHRmHU/WM8OHPdw9q9HH9uwfwr43JHE4u+CVJuHkhuXfA2gqfJCwXDiN5BSD7Xb3K7ebDGVgsClOHOr4ariAIzavBJPHGG284Mo4Wqb2Hnh7h3hxJzmfOqKja7a1xASLVWIE18zT6PpOv2tRUZbSw7WgWA3oEiJnPgtAKNKqxuLy8nIyMjDr/tXUDewSSU1RJdmFF7TbZNwylJAtVUZwYWfOypB0H1XrNpqbtx7KoMlqYPkws8iMIrYFNo5vOnTvHk08+SVJSEpIk1RneeebMGbsG6OoGdA/gm80pHE7O50b/SKBmGKzZakYty0dqxjLazmQ+fwipnR9yQGSD+5jMVjYdTKd3pC+dg5tvrRFBEJzHpieJl19+mSFDhnDw4EHatWvHoUOHuPXWW3nzzTftHZ/L8/EyEBHkRXJ6ae22X8pztI4nLdVUhTXzFNrIuKs2Ne0+mUNZpZnpQ8VThCC0FjYliaSkJJ588knat2+Pqqp4eXnx9NNP2zwCqrXrFNSOjPzy2qGwsk9HQGo1/RKW9BOgWK5aq8mqKGw4kE6Xju3p0an1zQ8RhLbKpiRhMBiwWCwA+Pj4kJ2djaIolJaWXuPItiE8sB3lVWZKy00ASFoDUofA1pMkzh9C8vBGDurS4D4HT+dTeKmaaTZUchUEoeWwKUkMHDiQ9evXAzB58mTmz5/P7373O4YOHWrX4FqKn4e/puf9Mh9A4xOGtaTlJwnVbMSScRJt5EAkqf4/F0VVWbc/jdAAT/p29XdwhIIg2JNNHde/blZ6/PHH6datGxUVFW2yUmx9fi5Ul5FfXvshKfuGYbl4FNViQtLqnRnedbFkJIDVhDay4XkkJ84WklVYwfyZvURdJkFoZWx6kvj8889/OUCWiY+P54477uDbb7+1W2AtiYebFv8ObqTnl9duq+m8VlFKsp0XWDOwnD+E5N4eTXD9ZblVVeX/t3fn0VHV9//Hn3fW7PtMIJAQtkDYkQjaCAihgCyC2no8B8GitQsKp/1JlYLHoqVHgt9iPTZiXQ7t6c9jT1uQHwFa+KpggRKTiLInLEFC9jBJyEaSWe7vj8gcApmQGTJLyPvxF5nPvXNf+YTkPfdz7/18dudcIi4yiMmpd7ZKnRAi8HSrSGRlZXX6+pYtW3o0TG+WFB/O5RuKhHOVul54h5Padg17TQm24m+wFR9Dl3wPiqbz/yoFxXUUldXz0JQktC62EUL0Xl0ONx05cgRoX740Jyenw0R2JSUlhIZ274nazMxM9u7dS2lpKdnZ2aSk3Pqp9MUXX6SwsND5dWFhIVlZWWRkZHTrGP6WaA7j67PVtLbZMRq0KBFm0Oqx15QQyItqWs8exl51AUejBbWxBkfjFbhpjW7dsPtd7r/nyLdEhBp4YFz3FyASQvQeXRaJdevWAdDa2sratWudryuKgslk4uWXX+7WQTIyMli2bBlLlixxuc2mTZuc/y4oKOCpp55i6tSp3Xr/QJBkDkMFSqobGTogEkWjQROdENB3ODnqKmg58D7og9FExKGExaLvn4ImLLb9wbmw2Pa5mkI6Xx7024p6Tn1byw8fHIpep/VxeiGEL3RZJD7//HOg/VP+jX/E3ZWW5t5ayP/85z9ZuHAhBkPvueCbaG6/eF1c1V4koP26hP3ySX/G6pK1KBeA0B/+Dk1YjNv7HzxejkGn4cGJ3VuoSQjR+3RrEHnTpk3YbDby8vLYtWsX+fn5zucmelpbWxvZ2dk89thjXnl/b4mNDCLYqLvpusRA1GtXcbQE5lTZtqI8tPHDPSoQdoeDrwqqGDcsjmCjrF0lxN2qW7/dRUVF/OxnP6OlpYX+/ftTXl6O0Wjk3XffZehQ1w9YeeLTTz8lISGB1NRUj/aPjfXeuskmU3iX7UMGRFJe0+zcrnlwChU5EGG3EGxK8Fqu7mS7WZullIaay8TOfppIN/cFOHaumvpmK7OmDLrtsd3N5kuSzTOSzTOBnM2VbhWJ9evX8/jjj/PMM884n6b98MMPWb9+PX/96197NNC2bdvu6CzCYmnE4VBvv6GbTKbw2y6e0z86mIPHy6msrEejUXBoYwGouXgOQ2hyj2dyJ9vNWo8eABRaTGNp82BRoE9zvsWo1zIoLqTLY3uSzVckm2ckm2cCNZtGo3T54brbczctX768w3QLTz31FAUFBXee8AYVFRV89dVXLFy4sEff11cSzWG0Wu1U1bXfHaQER6IYwwLy4rWtKBdtv+FoQqPd3tfucJBfWM34YbEY9XLBWoi7WbeKhNlsJjc3t8Nr+fn5mM3de3hqw4YNTJs2jYqKCpYvX878+fMBePbZZzlx4oRzu08++YQZM2YQGdn53TSB7vr0HNevSyiK0n7xOsCKhL22DEdNCboh7q/GB1BwqY7Ga1Ymp7q33KsQovfp1nDTL3/5S1asWMGDDz5IQkICZWVlHDhwgDfeeKNbB3n55Zc7vV32/fff7/D1z3/+8269X6BKiAtBoygUVzZw78j2AqqJGYj17CFU1eFy7iNfsxXlAcptFxByJa+gEqNBy9gh7l/wFkL0Lt36q5WRkcH27dudczYNHz6c7du3M2vWLG/n61X0Oi3940I63OGkiRkI1hbUBosfk3VkK8pF2z/Fo6Emm93BV4XVTBweJ89GCNEHdOtM4sMPP+SZZ55hxYoVHV7funUry5cv90qw3irRHNZhASLtdwsQOWpK0ESY/BXLyV5TiqO2FGP6kx7tf+ZSLU0tNiaPlKEmIfoCmbuphyWZw6ltaKWhuX1tCU30AFC02MpO+zlZO1tRLnc01HSmimCjltGDZahJiL7AJ3M39SXXn7y+XNXIqOQYFEMwuiH3Yi08iDHtURRDsF/z2S7mtQ81hbi/epzN7uDo2WomDjeh1wXG9RUhhHf5ZO6mvuTmIgFgGDsb24UcrIX/wTB2jt+ytQ81lWFMX+rR/qcu1tDcanNelBdC3P18MndTXxIRaiAyzEBx5Q3Tc5iHoI0fTtvJT9GP/r7Labe9zVaUC4qCbvAkj/bPK6gixKiToSYh+pBuz90kui/J3HFtCQD92NmoDdXYLn3tl0yqqn53V9NIj4aarDYHX5+r5p4UEzqtDDUJ0VfIb7sXJMWHUW5pwmpzOF/TJU9CCY/DemKvXzI5aktw1JV7/ADdyYsWrrXauVdWnxOiT5Ei4QWJ5jDsDpVyS5PzNUWjwTB6FvaKs9irv/V5JtuF60NNnj5AV0VokI7UQe4/WyGE6L2kSHiBc22JypuGnEZOA30QbT4+m1BVFWtRHtqEVDTBEW7v32a18/W5K0waIUNNQvQ18hvvBfHRIRh0mluuSyiGEPQjpmK7kIujqdZneRw1JahXK9AN9myo6URRDa1tdu6VB+iE6HOkSHiBRqMw0BzG5apbpwU2jPk+qA6spz7zWZ47v6upkrBgPSMHuX/BWwjRu0mR8JJEcxiXqxo7PIAIoIkwo0ueSNuZ/ai2Vq/naB9qykWbMMqjoaZWq51j5y2kjTCh9dOtu0II/5Hfei9JMofR1GKjpv7WQqAfOwdam7CeO+L1HA5LMerVSo/vajpxwUKr1S4P0AnRR0mR8JJEc8e1JW6k7ZeCJm4Q1hP7bjnT6Gm2ojxQNB4PNeUWVBERoiclSYaahOiLZAV7LxlgCkUBiqsamDA8rkOboigYxs6hZf972EtOoEsc1+l7qG3XaDu2B3tZAdqkceiH3Ycm/PYzyaoOB/aS47Sd3o/98nG0A8eiCere2rptVju1Da3UNLRSU9/C8fNXSB/XX4aahOijpEh4SbBRhyk6uNMzCQDdkMkoX/6dthP7bikSqt2G9cx+2o7uRG1pQBM9gLa8bbTlbUPbLwXd8O+hH3IvirHjBIuO5jqsBf/BWvAFaqMFJTgCw/j56MfO7jSDqqrsPPwtF8vrqW1opbahlcZr1g7baDUK3xvT7w56QgjRm0mR8KIkc9gtz0pcp2h16EfNpC1/O/aaUrQxA76bOiOP1rx/otZXoU1IxTjlcbSmwTjqq7GeP4Lt/BFaD/6Z1sP/F13SOHTD7qe5IYZrR/a0T/mhOtAOGI3+vifQJU9E0bj+EV+qbOD/HbpIv5gQ+sWEMGxAJNHhRmIijESHBxETbiQq3CjrWAvRh0mR8KJEcxj5hdVca7URbLy1q/WjZtD2dTbWk/tQh91H65d/x1F9EU3MQILm/h+0iWNRFAUATYQJ4z0PY5i4EIflEtZzR7Cdz8H27VEqAMUYhn7sbAypD6KJ7N4n/5xTlWg1CuuWTSI0SN+T37oQ4i4hRcKLEuPbrwOUVDcyfOCtF341QeHoh6djLfgCa8EXKKExBE1/Bt3wdJczxSqKgjYuGW1cMuqUx7GXnSE8GJojU1B0hm5nczhUvjxTybihsVIghBAu+aRIZGZmsnfvXkpLS8nOziYlJaXT7fbs2cOWLVtQVRVFUdi6dStxcXGdbtsbJN2wtkRnRQLAMP4h7FcuohsyGcOY77v1h17RaNENHEOYKZxr1bc+uNeVguJarja2cd9oud4ghHDNJ0UiIyODZcuWsWTJEpfbnDhxgj/+8Y/85S9/wWQy0dDQgMHQ/T+YgSg63EhokM7ldQkATWQ8oY++6sNU7XJOVxJk0DJ+aKzPjy2E6D18UiTS0m4/8+if//xnnn76aUym9ls8w8O7d8tmIFMUxfnkdSCx2ux8VVjFpBQTBrkoLYToQsDc/H7hwgUuX77MkiVLeOSRR3jnnXe8/qCZLyTFh1Na3YjDETjfy/EL7WtDyFCTEOJ2AubCtd1up7CwkK1bt9LW1saPf/xjEhISWLx4sVvvExsb5qWEYDK5f3Yzamgc+/Iu04ZCogf7d5c72Y7uPkNUuJGpkxLR+mDqb0/6zVckm2ckm2cCOZsrAVMkEhISmDt3LgaDAYPBQEZGBsePH3e7SFgs3vnUbjKFU+3mxWGAqOD2Lj5WUEmQl/4eu5OtucVK3ukKHpw4gJqaptvvcIc87TdfkGyekWyeCdRsGo3S5YfrgBluWrBgAYcOHWqftdRqJScnh5EjR/o71h1LiAslNEhHfkGVv6MA8FVhNTa7yv0y1CSE6AafFIkNGzYwbdo0KioqWL58OfPnzwfg2Wef5cSJEwDMnz+f2NhY5s2bx+LFixk2bBg/+MEPfBHPq3RaDTPuGcDRs9VU1jb7Ow45pysxRweT3K/3nfYKIXxPUe+Gq8M3CLThJoCrja38ast/mTo+gaWzR/Rwsu5nq21oZXXWYRamJ7N46pAez9GZQD3FBsnmKcnmmUDN1muGm+5mkWFG7h/dj8PHy2lobvNbjtwzlaggdzUJIbpNioSPzJ6cRJvNwf6vS/2WIed0Jcn9wukXE+K3DEKI3kWKhI8MiAtl3NBYPv+qBKvN7vPjl1uauFTRIGcRQgi3SJHwoTmTk6hvtnLkVKXPj51zqhJFgcmpsgypEKL7pEj40MikKAbFh7M3txiHD+8XUFWVL09Xkjoomqgwo8+OK4To/aRI+JCiKMyZkki5pZnjFyw+O25ReT1VddeYMireZ8cUQtwdpEj4WNoIM7ERRvZ+WeyzY355qhKdVsOkFBlqEkK4R4qEj+m0GmalJVJ4uY6L5fVeP57d4SD3TCUThsUSEhQws7AIIXoJKRJ+MG18AsFGLXtzvX82ceZSLfXNVqaMkruahBDukyLhB8FGHdMnDCC/oJordde8dpxrrTY+yy8hxKhjnCwuJITwgBQJP5k1aSCKAv+bX9LldlabnXJLk1t3Q9XUt/D3z8+z+p3DHLtgYeakgeh18qMWQrhPBqn9JCYiiMmp8fznWBkPP5BMaJDe2aaqKudLr3L4RAV5BVVca7UREaJn1OAYxg6OZfTgGCJCb13a9VJFA3tzi8krqEJVIW2kiTmTkxjcP8KX35oQ4i4iRcKP5kxO5MipCr74pox59w3iSt01/nuygv+erKCq7hoGffsdScMHRnK2pI5TF2vI+e5BvKT4MMYMjmXM4Bi+rW7i7/9bSEFxHUaDloxJA5mVNpC4yGA/f4dCiN5OioQfJcWHMyo5mn25xZy4YKHwch3Q/tDdwvRk7kkxEWxs/xE9OHEADlWluLKBk0U1nLxYw97cYvbkXAIgOtzID2cMZfr4BEJuOCsRQog7IUXCz+bdN4j/+ds3BBtbeWTaEO4fHe/yDECjKCT3iyC5XwQLvpfMtVYbBZdqCQ0PYog5FJ0PliIVQvQtUiT8bFRyDG+ufICIED2Kori1b7BRx8QUU8DOUy+E6P2kSASAyE4uQgshRCCQ8QkhhBAuSZEQQgjhkhQJIYQQLvmsSGRmZjJz5kxGjBjB2bNnO93m7bff5v7772fRokUsWrSIV1991VfxhBBCdMJnF64zMjJYtmwZS5Ys6XK7xYsX89JLL/kolRBCiK74rEikpaX56lBCCCF6SMDdArt7924OHTqEyWRi5cqVTJw40a39NRr3njUIlPe+U5LNM5LNM5LNM4GY7XaZFFX14WLLwMyZM3n33XdJSUm5pa26upqoqCj0ej2HDx9m9erV7Nmzh+joaF9GFEII8Z2AurvJZDKh17fPO5Senk7//v05d+6cn1MJIUTfFVBForKy0vnvM2fOUFpayuDBg/2YSAgh+jafDTdt2LCBffv2ceXKFaKjo4mKimL37t08++yzrFq1irFjx/LSSy9x6tQpNBoNer2eVatWMX36dF/EE0II0QmfX5MQQgjRewTUcJMQQojAIkVCCCGES1IkhBBCuCRFQgghhEtSJIQQQrgUcNNy+EJmZiZ79+6ltLSU7Oxs59PfBw4c4K233sJmsxEZGcnrr79OYmIiJSUlPPfcc879GxoaaGxsJDc3F4CLFy+yZs0a6urqiIqKIjMzk+Tk5IDINnPmTAwGA0ajEYDVq1czdepUn2QD2L9/P2+99RaqqqKqKs8//zyzZ8/2e7/dLpu/+62rNn/3W1dtPdlvtbW1vPjiixQXF2MwGBg0aBCvvfYaMTExfPPNN7zyyiu0trYyYMAA3njjDWJjYwE8bvN3thEjRpCSkoJG0/7ZfdOmTYwYMcKjvutRah+Ul5enlpWVqTNmzFALCwtVVVXVuro6dfLkyWpRUZGqqqq6Y8cO9emnn+50/w0bNqivvvqq8+ulS5eqO3bscO63dOnSgMl24/vcKXezORwONS0tzbntmTNn1AkTJqh2u11VVf/22+2y+bPfbvfz9me/3S5bT/ZbbW2tmpOT4/x648aN6q9//WvVbrers2bNUvPy8lRVVdWsrCx1zZo1qqqqHrf5O5uqqmpKSora2NjoUR5v6pPDTWlpafTv37/Da5cuXSIuLs75hPf06dM5dOgQNTU1HbZra2sjOzubxx57DACLxcLp06dZsGABAAsWLOD06dO37OePbD3Nk2wajYaGhgag/SzHbDaj0WgCot9cZetp7mbrqs3f/dbd/4s9ISoqiilTpji/njBhAmVlZZw8eRKj0eicWfqJJ57g3//+N4DHbf7OFsj6ZJHozODBg7ly5QrHjx8HIDs7G4Dy8vIO233++efEx8czevRoZ3t8fDxarRYArVaL2Wy+ZT9/ZLtu9erVLFy4kPXr11NfX99juW6XTVEU/vCHP7BixQpmzJjBc889R2ZmprPdn/3WVbbr/NVvXbX5u9+683/RG/3mcDj4+OOPmTlzJuXl5SQkJDjbYmJicDgc1NXVedzm72zXLV26lEWLFvH73/+etra2O8rVU6RIfCc8PJw333yT119/nUcffRSLxUJERITzl/G6bdu2ee2TujeyffTRR+zcuZNt27ahqiqvvfaaz7LZbDb+9Kc/8c4777B//362bNnCL37xC5qamno0gzey+bPfuvvz9pY7yeatfvvtb39LSEgITz75ZI+8X0/qqWwHDhxg+/btfPTRR5w/f56srKweSniH/Dzc5VddjZ9WV1erY8aMUZuampyvVVRUqOPHj1dramqcr125ckWdNGmSarPZVFVVVZvNpk6aNEm1WCx+z3azgoICdcaMGXeUy51sx48fVx966KEO7XPnzlWPHTvm937rKtvNfN1vXbX5u9/caeupftu4caO6fPlytbW1VVVVVT127Jg6f/58Z7vFYlEnTJhwR23+znazzz77TH3yySfvKFtPkTOJG1RXVwPtp4+bN2/miSeeICQkxNn+ySefMH369A7rW8TGxpKamsquXbsA2LVrF6mpqcTExPg9W3Nzs3PMXVVV9uzZQ2pqao/m6ipbv379qKiooKioCIALFy5gsVhISkrye791lc3f/dZVm7/7ras2b/Tb5s2bOXnyJFlZWRgMBgDGjBlDS0sL+fn5APztb39j7ty5d9Tm72xXr16lpaUFAJvNxt69e73yf84TfXKCP1cz0q5bt46jR49itVpJT09n7dq1zlv5AObMmcO6deuYNm1ah/e7cOECa9asob6+noiICDIzMxkyZIjfs12+fJmVK1dit9txOBwMHTqUl19+GbPZ7LNsO3fu5P3330dR2le/WrVqFbNmzQqIfnOVLRD6ras2f/ebq7ae7rdz586xYMECkpOTCQoKAmDgwIFkZWVx9OhRfvOb33S4lTQuLg7A4zZ/Zvv666955ZVXUBQFm83GxIkTWbt2LaGhoR71XU/qk0VCCCFE98hwkxBCCJekSAghhHBJioQQQgiXpEgIIYRwSYqEEEIIl6RICCGEcEmKhBBCCJekSAgRgGw2m78jCAFIkRDCbR988AErV67s8NqGDRvYsGEDDQ0NrF27lgceeICpU6fy5ptvYrfbASguLmbZsmVMmTKFKVOm8MILL3SYJXXmzJm89957LFy4kAkTJkihEAFBioQQbnr44Yc5ePCg8w+8zWZj9+7dLF68mDVr1qDT6di3bx87duzg8OHD/OMf/wDa5zP66U9/ysGDB/nXv/5FRUUFb7/9dof33r17N++99x75+fnodH1y4UgRYKRICOEms9lMWlqac8GYgwcPEh0dTb9+/fjiiy9Yu3atczK+H/3oR+zevRuAQYMGkZ6ejsFgICYmhuXLl5OXl9fhvZcuXUr//v2d8wEJ4W/yUUUIDzzyyCN8/PHHPP744+zcuZNFixZRVlaGzWbjgQcecG7ncDicK79duXKF3/3ud+Tn59PU1ISqqkRERHR435tXiRPC36RICOGBWbNmsX79es6ePcuBAwf41a9+hU6nw2AwkJOT0+lQ0ebNm1EUhezsbKKiovj0009vWZTn+oy0QgQKGW4SwgNGo5E5c+bwwgsvMHbsWBISEjCbzaSnp7Nx40YaGxtxOBwUFxeTm5sLQFNTEyEhIYSHh1NZWckHH3zg5+9CiNuTIiGEhxYvXszZs2dZtGiR87VNmzZhtVqZN28e9957L6tWrXIu0vP8889z+vRp0tLS+MlPfsLs2bP9FV2IbpP1JITwUFlZGQ899BCHDx8mLCzM33GE8Ao5kxDCAw6Hg61btzJv3jwpEOKuJheuhXBTc3Mz6enpJCQkyHUFcdeT4SYhhBAuyXCTEEIIl6RICCGEcEmKhBBCCJekSAghhHBJioQQQgiXpEgIIYRw6f8DkSlT8vuOx+EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xU9aLmpqpRgI",
        "colab_type": "text"
      },
      "source": [
        "With a simple pivot table and ``plot()`` method, we can immediately see the annual trend in births by gender. By eye, it appears that over the past 50 years male births have outnumbered female births by around 5%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "whKejc3EpRgJ",
        "colab_type": "text"
      },
      "source": [
        "### Further data exploration\n",
        "\n",
        "Though this doesn't necessarily relate to the pivot table, there are a few more interesting features we can pull out of this dataset using the Pandas tools covered up to this point.\n",
        "We must start by cleaning the data a bit, removing outliers caused by mistyped dates (e.g., June 31st) or missing values (e.g., June 99th).\n",
        "One easy way to remove these all at once is to cut outliers; we'll do this via a robust sigma-clipping operation:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tsbx01BpRgJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
        "mu = quartiles[1]\n",
        "sig = 0.74 * (quartiles[2] - quartiles[0])"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbw7Y7Eu_1ES",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3158d3c2-d59c-4b08-8b8d-87e264fce2ba"
      },
      "source": [
        "quartiles"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4358. , 4814. , 5289.5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6h7xfRXEpRgL",
        "colab_type": "text"
      },
      "source": [
        "This final line is a robust estimate of the sample mean, where the 0.74 comes from the interquartile range of a Gaussian distribution (You can learn more about sigma-clipping operations in a book I coauthored with Željko Ivezić, Andrew J. Connolly, and Alexander Gray: [\"Statistics, Data Mining, and Machine Learning in Astronomy\"](http://press.princeton.edu/titles/10159.html) (Princeton University Press, 2014)).\n",
        "\n",
        "With this we can use the ``query()`` method (discussed further in [High-Performance Pandas: ``eval()`` and ``query()``](03.12-Performance-Eval-and-Query.ipynb)) to filter-out rows with births outside these values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U96SZ3bLpRgL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnftFBUApRgN",
        "colab_type": "text"
      },
      "source": [
        "Next we set the ``day`` column to integers; previously it had been a string because some columns in the dataset contained the value ``'null'``:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dJxe-bUhpRgN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# set 'day' column to integer; it originally was a string due to nulls\n",
        "births['day'] = births['day'].astype(int)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "475pw0MjpRgP",
        "colab_type": "text"
      },
      "source": [
        "Finally, we can combine the day, month, and year to create a Date index (see [Working with Time Series](03.11-Working-with-Time-Series.ipynb)).\n",
        "This allows us to quickly compute the weekday corresponding to each row:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CGf2ss4IpRgQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create a datetime index from the year, month, day\n",
        "births.index = pd.to_datetime(10000 * births.year +\n",
        "                              100 * births.month +\n",
        "                              births.day, format='%Y%m%d')\n",
        "\n",
        "births['dayofweek'] = births.index.dayofweek"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZJnSSHMpRgR",
        "colab_type": "text"
      },
      "source": [
        "Using this we can plot births by weekday for several decades:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5WSY_11pRgS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "c8110c9e-1d7f-4dea-feb1-3770c6abe9b3"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "\n",
        "births.pivot_table('births', index='dayofweek',\n",
        "                    columns='decade', aggfunc='mean').plot()\n",
        "plt.gca().set_xticklabels(['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun'])\n",
        "plt.ylabel('mean births by day');"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1p3iEN9pRgU",
        "colab_type": "text"
      },
      "source": [
        "Apparently births are slightly less common on weekends than on weekdays! Note that the 1990s and 2000s are missing because the CDC data contains only the month of birth starting in 1989.\n",
        "\n",
        "Another intersting view is to plot the mean number of births by the day of the *year*.\n",
        "Let's first group the data by month and day separately:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VH9ZODYmpRgV",
        "colab_type": "code",
        "colab": {},
        "outputId": "93bb3f2f-2778-4413-e124-a7aee353f39b"
      },
      "source": [
        "births_by_date = births.pivot_table('births', \n",
        "                                    [births.index.month, births.index.day])\n",
        "births_by_date.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1  1    4009.225\n",
              "   2    4247.400\n",
              "   3    4500.900\n",
              "   4    4571.350\n",
              "   5    4603.625\n",
              "Name: births, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8vBhbSI2pRgX",
        "colab_type": "text"
      },
      "source": [
        "The result is a multi-index over months and days.\n",
        "To make this easily plottable, let's turn these months and days into a date by associating them with a dummy year variable (making sure to choose a leap year so February 29th is correctly handled!)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7CBAoaYpRgX",
        "colab_type": "code",
        "colab": {},
        "outputId": "ae7bd8a6-c911-4c90-f86a-305868ced55b"
      },
      "source": [
        "births_by_date.index = [pd.datetime(2012, month, day)\n",
        "                        for (month, day) in births_by_date.index]\n",
        "births_by_date.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2012-01-01    4009.225\n",
              "2012-01-02    4247.400\n",
              "2012-01-03    4500.900\n",
              "2012-01-04    4571.350\n",
              "2012-01-05    4603.625\n",
              "Name: births, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCNeuucPpRgZ",
        "colab_type": "text"
      },
      "source": [
        "Focusing on the month and day only, we now have a time series reflecting the average number of births by date of the year.\n",
        "From this, we can use the ``plot`` method to plot the data. It reveals some interesting trends:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGkRQCeCpRga",
        "colab_type": "code",
        "colab": {},
        "outputId": "57316a3c-83d7-4fe6-be58-8e9112d5450c"
      },
      "source": [
        "# Plot the results\n",
        "fig, ax = plt.subplots(figsize=(12, 4))\n",
        "births_by_date.plot(ax=ax);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yYhU6hxyIRGOCH7nMkF3wihgGW1eVYHOzGSNWL3pHXSgvVkEqyX+zKjEohYSb\nQqwWPHd/tBHf+tKWvEl+V8KqhJgLhGK4bWtVQREqU5ES//rlHbhzG5ccmEkku31hPPtGF85fTm3p\n7c2x4Fkq8L7kV08MQiYVYe/tzQCATyeiye9eyF6WkCBmC28lm20kOa9I7uzshN/vx/79+3Hffffh\nwoULaG9vx9atWwEAO3fuxPHjx9Ha2ootW7ZAIpFAo9Ggrq4OnZ2dOHPmDHbu3Ckce+LEiTlNkCAI\ngkiFZVmc7pyETCrCX36Wa6nMR4zT8QYiUCsylwzTqWUo0SvRO+ZOaR2dSyQDnOUiHImje9iF8YRI\nLjXmjwrftrWKGz+y+5HTYRIVMgCgqYCkvasJbwWRS8W4dXPVrJ4rEjHQqqVwe1NF8qUBO779i1P4\nn7OjeCmpxByQ1G0vTyORa5n6pLrN93y0ESWJUnzN1XqUGJQ40zU5wyNPEHPFlzhn1CmR5HnwJCsU\nCuzfvx/PPPMMHnvsMXz9619P8Qqp1Wp4vV74fD5otdO+MpVKJTzOWzX4YwmCIIgrZ9Tqg8URwPqG\nYpQkxKk7y7a9xx/JKaoaK3UIhKIYT2odnU8k85aLC71T05HkAkTyisoiodtdoSIZAD7zkVp8alsN\nmmsK9xdfDerLtdjQWIy7Ptowp+hukVqWEkm2u4P48fMX4QtEoFNJMTLpS2ncku97WQpUmTUo0siw\nutaAW7dMLywYhsHNLeUIR+M41XHl7bsJApj2JKuV8+xJrqurQ21trfBvvV6P9vb26Tf2+aDT6aDR\naFIEcPLjPp9PeCxZSOfCbC7suKXMcpzjcpxTOst5jst5bjzLaY5vnh0FANx6Q63QgjkcZ2fMkat3\nHEFNmTbr/DesLMWJSxZMukPYuJrz1MZYLupcU6WHOUOi3A69Cj97uQMn2ydRXcr9fW1zSUFJb1/6\n9Go88dwZ3LKluuDv5IaWStzQUpn/wEXgn75885yfazaoMGTxIhCKwmzW4he/60QoEsPf/OlGjEx6\n8eI7l+EIRNFSyS0OgokuYdUVRUvq95w+1p/93e2QSkRC+3KeP961Ai8d68MHHRbs/sSqqznEK2Yp\nfR+zZSnPjWW431hl2fQ5YzLmz1PIeyV7/vnn0d3djUcffRQWiwVerxc7duzAqVOncOONN+Ldd9/F\ntm3b0NLSgieffBLhcBihUAh9fX1oamrCpk2bcPToUbS0tODo0aOCTSMfVuvyLgFjNmuX3RyX45zS\nWc5zXM5Zl6RMAAAgAElEQVRz41luc3z37AgkYhFqzSoUJUTypM03Y47eQARxFpBLRFnnX5roeHe+\n04JNiQix1c4FOMKBcNbn7dxQjldPDKK93w69RgavO4BC9gsbSzX4yd/uBMMwBX0ny+27S0aZiGg5\nPEGcarXhvQtjaKjQYUO9AfEIZzk4fWkcZUVysCyLExfHoZCJoZWJl8xnMtvvb219MS722dDWbUFp\nDp/7tcRy/o0u9blNORLXsuD0tSwcyt/AJ69Ivueee/Dwww9j7969EIlEePzxx6HX6/H3f//3iEQi\naGxsxB133AGGYbBv3z7s3bsXLMviwQcfhEwmw549e/DNb34Te/fuhUwmwxNPPHGFUyUIgiDs7iBG\np3zYuMIEpVwitCjO5En2B2f68dKpKlFDLhWjO6nZh49P9ssRGf7Ypkr87oMhxFm2IKtFMtdKq+TF\npkjDJSQ63CG8fnIIAPClTzQLnQqB6aTKQYsHNncQ29aU5mxYstTZ3GzCxT4bWi/bcPsNKpzttsJU\npEBN6dKNZhKLB1/dIsWTXEDCcF6RLJVK8YMf/GDG4wcPHpzx2O7du7F79+6UxxQKBX70ox/lHQhB\nEARROHxDDt7TKxIx0KqkGdv6BhN+VoUs+01BLOIahJy/PIVJhx8lBhU8fLJfjnJrRp0CW1eZcapj\nctYimeDgm684PSEMWTwwFSlQV8Yltuk1cpiKFOgdc4NlWZxJtA/fstK8aOO9GqxvNAHowoXeKaxr\nMOLHL1zEymo9vvnFzYs9NGKBaeuzobxYjeKimaUs5wqfuJda3YKaiRAEQSxLrM4AAMCcqAoAcGLL\nk0EkhyKcSJbnEMkAsL6Rq5F8MdEgxBeIQF1ActgdN9VALhVjdZ0x77HETPjSdv1jLrj9kRmNUhor\ni+ANRDDpCOB0lxUyqQjrGoozvdSywaCVo7ZUi64hJ149MQgAsLmDizwqYqGxOPz44ZELePy5MylJ\nyJdHXPjvt7oRi8fn9Lr+YBRymTjF/y7Lcz0ESCQTBEEsSaZcnGAw66ejLTqVFIFQDJFoLOVYvjJC\nvmxuvotea68NLMvCG4hCW4BIrivT4Sd/uxM3rCqZ1RwIDl4kn+/mosRVaRU/GhPl0g78vgsWux/r\nG4oLysxf6qxvLEYszuJ42wQAwOEJpZQoJJYffAdPmzuEH794EZFoHPE4i2de68Bbp0fQPz43X7Qv\nGBVa1PPMS51kgiAI4tojUyRZmxBb6b7kQiPJxUUKVJrV6BxywOULI86yBZcZK7QDHjETPumyZ9gB\nYGZZvNV1RjAAOga5vxfS0W85sGGFSfi3iGEQi7NCIxViedLWZwMArKkz4PKICwd/34VTHRahxORE\nUonK2eALRqCSp17LCum4tzQbvxMEQVznWJ1BSMSMUPoNAHQqTiS7/eGU1tSCJ7mAm8L6hmL87uSQ\n4H1NritKLAx8JDmeCJJWpbXQrjSp8fj92xEMx6CSS+bVq3ktU1euRZFaBn8oii0rzfjgkgUOT0jw\ncBPLi0g0js4hJ8qMKnz17vV4/LmzeO/iOD7smhSO4XMxZkMsHkcwHINGSZFkgiCI6wKrM4DiImVK\nBJdvFpLeUKTQSDIw7Uvmu7xplSRIFhqFTAxZolKFVCJCiUE54xizXonqEs11I5ABLnr8N3evx4N/\nukGIrts9M33JHYMO/Oqdy4jG5uZXJa4NLo84EYrEsK7eCJlUjL+5ez2KNDKEwjHhujQXkTzdkjo1\nkkyJewRBEMuQQCgKbyACc5pgEiLJvjS7RYGeZABoqtLjY5srEUkIDnMGwUbMLwzDCNHRCpMaYhHd\nmnkaKnRYWWOAIbFj4vSEZhzz8vv9+N0HQ3jm1Q7yLC9h2vo5PzLfydOgleNvd2/AzS3l+LM7VkEp\nF2Pc5pv16/oEkZwaSSa7BUEQxDJkOmkvVcBOe5JTI8mFlIDjEYkY7PvESvzpx1ZgxOoV2kcTC4te\nI8eUK4jqDJ0NCU4wAYA9TSSzLItBC9e+5mS7BcU6Be7Z1XjVx0dcOZf67ZCIRVhZbRAeqynV4i/u\nXA2Aa3k/ZPEiFo/PaiHJt6TWpEWSRQyTt9Y4LVcJgiCWGJmS9oBpb2t6reTZ2C145FIxGiuKZrQM\nJhYG/rtL9yMTHIaEx96RJpKtriACoSjW1Rth0Mrx1plhsBRNXnLEWRajUz5Ul6izXqfKjCrE4qwQ\nJCgUf5ZIMpB/d42ufgRBEEsMXiSb0uwW057kLNUtroOyYUsVvuteevk3gsPAdyVME8lDE1xJsNW1\nBjRWFiEcicPpnVkrnLi28QYiiMVZGLTZPfdlxdwCcrYVLvhGIplqvufzJZPdgiAI4hqBZVn8z9lR\nFOsU2LCiOGvb5ilnFruFKrPdYjaeZGJxuHVzFcxGNVbW6Bd7KNckUokYGqV0pkie5ERyTakW/hAX\nMbTY/YI9g1ga8F5zvSZ7onB5oqPnhN2PDbN47emW1DMlbz5fMolkgiCIa4TeUTeee7MbAFBbqsVX\n/mQdTPqZiXNWV2a7hVwqhlwmnmG3mI0nmVgcKkxqbFhdBqt1bs0SrgeMWjksjgAGJtx48sgF/OVn\n1mBwgvMj15RqBAE94fBjVa0h10sR1xh89D+5pGU6ZUkieTb4gzNbUvPkE8lktyAIgrhGuJgopF9b\npsWgxYNXTgxkPM7qDECtkGS86OtU0qwl4ArJ5iaIaxW9Vo5QJIbffTAEjz+CX7/TiyGLB0adHFqV\nDKVGbtE4aQ8s8kiJ2eL0cgucXDsAJQYlGMzBbiFEkjPZLUgkEwRBLAna+m0Qixg89IWN0GtkON1p\nRSSaWvt1yOLBpCOA0kRUJR2dSgaPP5KSvBSKxCARiygJj1jSGBMC6nSiucTQpBcuX1iowFJq4M4J\ni2NuXdmIxWPabpFdJMukYhQXKTA+y0gyX90ik92CRDJx1ekeduKhp49jbGr29QwJ4nrF4w9jYNyD\nFZVFUCmk2LamDP5QFK29NuGYSDSGn73cjlicxR/vqMv4OlqVDLE4K/gzAc6TXEjhfIK4luGjjCwL\nrErybtckRLJWJYVSLobFcf1Fkpd6IxU+kpzLkwxwtiS3LwyXr/DkzGzNRID8iXt01VxmWJ0B/Ofv\nOhEMR/MfvEBc6rfD5g7iVIdl0cZAEItJJBrDibYJ/PDwefz+1FBBz7k0YAeL6UL629aWAgA+aJ8A\nwLVWPfhGN0anfLh1cyXWN5oyvg7fRprfYgQ4TzL5kYmljj5pK/5ztzRgQ6ILGx9JZhgGJQYVJh2B\nvE1F3r0whjcKPDevdY6eH8WXf3gU7QP2xR7KnBE8yXkSLhsqdACAvjFXwa/tC0TAAFDJyZN83XOy\n3YJ3L4yhrW9+ThaL3Y9vP3MKHbM4+fjkifZBx7yMgSCWGj95sQ0/e6Udbf12vHJ8APF4/rqt/Dm7\nrp678VeXaFBhUuPCZRuOtY7hqV+14r3WcVSZNdj9sRVZX0easFTEkiJLoUgMchnlaRNLG2OiPJhe\nI8OKqiLs++RK3LWzAS2NRuGYUoMS0Vgcdnf2WrouXxjPvtGFI2/3IhBavIDSfOANcN7saIzFgde7\nEE7kHyw1HN4QpBJRRiGbzLRIdhf82r5QFEq5BCLRzGpBmfI6kiGRvMzgt1g9gUieIwvjt+8PYMTq\nxcmOyYKf4/BwF6f+MfeiRrQJYrEYGHdDr5FhU5MJvmAU/RO5L+gsy+JSvx06tQzVpVydXIZhsKOl\nDNFYHP/xWicu9duxvrEY3/ri5pw+OnFCJEdjqZ5kKv9GLHUqTGpIxCLcvL4CIoaBUafAZz5Sl9J9\nbdqXnN1y8fbZEURjLOIsi97RwiOSs8UfjOKJw+dntasaZ1kMWTwFt9d+6VgffMEoyowqTDoDePn4\nwBxHu7g4vSHoNbKsZS95GsrnIJIDkaxi+BM3VOd8LonkZQa/KvbMwq+TjSlXACfbuZN70FJ4WSK+\nbWgszqJ72HnF4yCIpUQoEoPbH0F5sRrb15YBQN6dnQm7Hy5fGKtq9BAl3SQ+cUM1vrFnE+771Cr8\nrz9ag6/evT5v5EMqiGQukhyLxxGJxsmTTCx5DFo5nvjKR/C5m+uzHjNd4SJzclckGsPb50aF/+9a\nwHvU2+dGcKnfjtc+GCzo+ClXAD84dA6P/ceHOJo0xmxY7H68c24MpUYV/v7eLSjWKfD6ySFYZpnY\nttjE4nG4fWEYciTt8agUUpQZVegfdxe0QwcA4Ug8q93MVDSzxGYydNVcZggi2X/lkeQ3Tg0jzrIQ\nixiMWr0FJwY4vSHhRt8+QJYLYnlgcwULqs/Jb/MWFymwps4AEcOgrX86+S4ai+NyWvSqZ4T7/5XV\nqY0kxCIRVtUasHNDBbatLcu4XZiOWMwI7wMAoTD3XwXZLYhlgFYly3ke5Iskf3DJAo8/go9troSI\nYdA1tDAiORSJ4Y0PhwEAQxav0CUzG95ABP/wn6fRmRjPxQIsk+0DdsRZFp+6qQYqhRSfv3UFYnEW\nLx7ru/IJJPH6ySHsfvgVHHyjC7ZZtoQuBLcvApbN70fmaazQIRiOYdxWWHGASCw+58o+JJKXASOT\nXqGjFp/Fmd5MYLYEQlG8e2EMxTo5tq8rQzTGYtSa/wcZCEURCMXQXF0EiViEjgX0Jf/2vX788Mj5\ngrelCOJKeOrXF/D4s2fyRi/4m4ipSAGVQoqGCh36xtxCGaK3To/gewfPoGto+tzgb9RN1VfebU2S\nZrcQWlJT4h5xHcCXRmwfcMzoPAkAJy5xibB3bqtFbZkG/eNu4RzJxYjVi9+8149LBebnHLswBo8/\nglIDF6k8123NeXzHoAPeQAS3b62GWa9A97Az772N3+GtT1gQtqw0o7ZMi1Mdkxiaxe5vPs73WBEM\nx/D22VE8/tzZeb/nTle2KEwkz8aXzLIsotE4pJIFFMk2mw27du1Cf38/Ojs78fnPfx5f/OIX8cgj\njwjHHDlyBHfffTe+8IUv4J133gEAhEIhfPWrX8UXv/hF/NVf/RUcjsWPKk45Ayn1Q5cyU64AfvzC\nRXz7F6fw66O9AJIjyTMvDoFQFP966Bzevzie97UHJzwIR+O4YXUpGhM/yEIsF3zSXolBhaaqIgxP\nemc0NpgvTndNoq3PjvFZFhYniNlidQYwavXB7Y/kLW04xUeSdVyS0bp6I1gW6EjsqvAWpIGJ6fOp\nZ8QJtUKCCpP6iscqSYsk83kB5Ekmrgc0Sim2rDRjxOrFo784heFJr/C3cCSGy6Nu1JRoYNQpsLLa\ngFg8vy/5v17vxLefOYXfvNePg6935R0Dy7J448NhyCQifOWuFjAAzuQRyZ2JgNINq0vQXK2HPxTN\nG5ganPBCIhahvJhbGDAMg7s/2gAA+PXR3nnROizLYtjqRaVZjY0rTLC5g3mj4rOlkBrJyTRUFAEA\n+sbzi+RYnAULLFwkORqN4tFHH4VCwV3wf/zjH+OBBx7Ac889h1AohHfeeQdTU1M4ePAgDh8+jJ//\n/Od44oknEIlEcOjQITQ3N+O5557DZz/7WTz99NNzGuR80TXkwDf+7QROzSIJ7Vpl3ObDd/7jQ5xN\nnHj8Fq8/h93i6PkxdAw6ChLJ/IWlpkSDujJOJCff1LPBi2SjVo6WBi5L/1xP7ovDXOHfq4d8z8QC\n09Y/HT1Kt0qkkxxJBoC1iZJubf02sCwrXNh5sW13BzHlCqKpKtWPPFckaZ7kcIS3W5BIJq4P/vpz\n6/AnOxvg9IbxuyQ/cM+oC9FYHKvruJbVzYmdm1yWiylnAO+eH0OpQYnaUi0mnYG8lgNvIIIpVxBr\n6oyoMmuwoqoIl0dcOWv7dg45IJeKUVemFcaVK6cnGotjdMqLKrM6RQCurTNida0BbX12HG+byDnO\nQphyBREIxdBQqUdTNSdOhy3ePM+aHYXWSOapNKshlYjQX4BI5q+DCxZJ/ud//mfs2bMHJSUlAIA1\na9bA4XCAZVn4fD5IJBK0trZiy5YtkEgk0Gg0qKurQ2dnJ86cOYOdO3cCAHbu3IkTJ07MaZDzBX9z\nuzyycNmsVwOXN4Qnj1yALxjFno83gWG4kxKYjiSn2y0i0Tje+JCrCTlk8eZdYfIimS9DJRYxGMyT\noQ9MC1eDVo6tK80AgNNd8y+Sw5GYUAe2e4RE8nJhwu7H3//8JAYLWJDN6fVtPrzwbt+sq6609U17\ninvyXD/4GygfSa4v00GtkKAtUT+c31kZS/jp+Bth8zxYLYBpkRxL2C34uVJLauJ6QcQw+Mz2Wijl\nkpQdUL6O8Jo6buHaXF0EBrnF6Lut42AB3Lm9Dh9ZxyXitg/mtlxMJiKtJQmrxZZmM1hwtoVMuLwh\njNv8aKribIp8bkKupMKxKR+iMRa1ZdqUxxmGwZ9/ahWUcjGefbMbk1fYfZDXAvUVOlSbNSmP5aJ/\n3I1THZaCotmOWdotJGIRDFq5UFs5F7ztbEEiyS+88AKKi4uxY8cOsCwLlmVRW1uL7373u7jzzjth\nt9tx4403wuv1Qqud/qJUKhW8Xi98Ph80Gu5DVavV8Hrnd/UxW/h+3+P2pd0J7sDvuzDlCuJzN9fj\n9huqoVZIBcEYCHHeKm8gkuKd/ODSBJzeMBiGizbnWwkPT3LbOGXFKkglIlSZNRie9OVN3uPLvxm0\ncpj0StSVadEx4BBE/HzBrzwBiiQvJ1ovT2Fsypd3a3KuvPDOZbxyfAAvvttf8HOisTg6Bh0o0Suh\nVkjybs1OuYMQMQwMOu6CLxIxWFtvhN0dwvGL05GdsSk/WJZFd0J081GaK4W3W0T4xL2E35IiycT1\nBMMwqC3VYMLmFxaKHQMOiEUMmqs4EapSSFFdokHvmBuR6Exfciwex3utY1DKxbhhVYkQge7Mk2sz\n6UgVyetXcI1/LmVJZOfF8Kpa7vXNeiX0Ghm6h51ZRSYv/vlGKsmY9Ep86faVCIVjOPw/l3OONR+8\nt7m+ogjVJZyeG7Hm13LPvtGNf/vNJTz9UpuQK5UNp4cTu4YCE/cAQKeSweuP5PVHR6LcdZC/Ls6W\nnOnOL7zwAhiGwfvvv4+uri5885vfREdHB37zm9+gsbERzz33HB5//HHccsstKQLY5/NBp9NBo9HA\n5/MJjyUL6XyYzYUfWyhTiSinxREo+PUnbD68eWoIn7+ted4jMXOdY9+4G2XFKvzF51rAMAyKNDL4\ng1EYjWrhhsiygEItR1FiZfbW2VFIxAw+ua0Or77fD2cwitVZ3j8Wi2PM5kNtuRZlpdyNe1W9EYMW\nD4JxoL4s+7gDUe4H21BrhNmsxa4t1fjPV9txedyD22+qndN8M2FxT4tkmzsEVixGSSJhY6FZiN/m\ntcJiz82RsAnZPKEFGUtbosXzH84M485bGtBYlT9629Y7hWA4httuKMOE3Y/THRZIFFIYEo0N0nF6\nQijWK4RzBwC2r6/AqY5JvHVmBACXxe30hCCWS9Ez4oJcJsbWdRVzjnbwmM1aGPTceaBSyWA2ayEb\n5XaATAbVon+/V8pSH38hLOc5Xu25raovRueQE55wHAaDHIMWD9bUF6Oqcvq837iyBEPH+mD3R7Gu\nMfV6cCoRXPrUR+pQValHZUUR9Bo5uoadMJk0GWv6ms1a+MJjAICmumKYzVqYTBqY9Ep0DTlRXKyZ\nUZ1j4ChXjWLb+grhM1q/wox3z48iDAZVGT43q4u7B25YVZrxc/2jXRoceecyJp3BK/rcJxMBtYbK\nIhh1Cug1coza/Hlfk/ctn+myYnjSi6/csxGbV3GOhLEpL577XSf+6q710Kll8CcKDzTWFUOZp5kI\nj8mgxOVRF5RqBXTq7DaNWKKGtlYjn9PnkHM0zz77rPDve++9F9/5znfwla98RYgOl5aW4ty5c2hp\nacGTTz6JcDiMUCiEvr4+NDU1YdOmTTh69ChaWlpw9OhRbN26teCBWa3zu93KsixGEisimyuIoRFH\n3i+DZVn8y3Nn0TPigk4hxkfWlc/beMxm7Zzm6AtG4PKGUVuqxdQUtzBRSMWYsPkxNJoaUe0fdqDS\npIbDE8KwxYMNjcVortThVQBtPVasyCJ2R61eRKJxlBtUwhhNiRXexe5JaLLUWzWbtRibTMwpEoPV\n6sGqRHTs7dPD2NhgzPg8ADjeNo5Db/UgFImh0qTB3+3bDKkk+6Kkf4RbkZfolZh0BvDBhVFsT2yF\nLSRz/d6WAtfC3AYSUdqBMde8j8XtD2PY4oFBK4fDE8KPfnkOj9y7Ja8P+L1znLBtKNdCKmZwugM4\n1TqGzc3mGcdGY3HY3EE0VRaljL8mkZDnTbRHvXFVCd74cBh/ODmIUasXG1eY4LjCHS7++/P7uZun\nwxmA1eqBNXGdiISji/79XgnXwu9zoVnOc1yMuZkTuzkXOi0YGnWBZYGmCl3KOKoT5+bJi2Mo1aVG\nMl97nxOvNzabhec0VxfhVMckLnZZUF6cmmjLz7E/YQGUM6zwvFXVerx3cRxnL40jGoujfdCBO7fX\nQsQwONc1CblMjCKFWDi+poR77VOtY5BvqMDghAf+YASrE1aRzgE7RAwDtYTJ+rkqZBJ4fKEr+twv\nDzuhVUlh0MphtXpQaVLh0oADg8OOrDXb/cEIvIEI1tYb0VCuw6snBvHoz05gz21NuH1rNX79Vg/e\nPT+KpkoddrSUw2LzQSmXwOsOoFC/gTzhMe4fsudMeLYkcj9iCU2SiVziedZhi3/6p3/C//k//wf7\n9u3DoUOH8OCDD8JkMmHfvn3Yu3cv7rvvPjz44IOQyWTYs2cPenp6sHfvXvzqV7/CAw88MNu3mzc8\ngYhgSQBQUEWEDzsnBf9hcuJOPnhrykLAW0bKkqKmaqUUsTgrZIjy8A1F+DIpjZVFqElszQzlMN4n\n+5F5+DI21hxdjAAuiiaXiqGUcwK3RK9ETYkG7QP2nO0yz3RZ4QtGoddwq/03T4/keR9ubjeu4Vam\ns/UlW+z+vFtAxNVnPFGHeNIRKLgud6F0J5Jzdm2qxNaVZvSPuwvqtsVvazZX6bGiklv0ZctrcHhC\nYFmuRnIyBq0clWbuQl5hUgsljH5/issT2LCieA4zyowkETlJt1tQdQvieoO3IgxaPELXu7X1qcGa\n5kQgJz15LxKNo63fjhKDEjWl0/dC3s+cqwfApNMPsYhJuQ6sSVg1Wvts+OlvL+HFd/vQ2mtD/7gb\nFrsfa+uMKZ0DhWtN4hr1/37Thh8euQCHJ4R4nMXwpBflJlXOHW61QgJfMDpnPeIPRjHlCqK6ZDpq\nXl3Cfaa5LBdWJxd9LjOo8Cc7G/Dt+7ZCJhXhnUSDlK5h7rPjG4/ZPUEYdYVbLQCuXjaQuZJXMvx9\nZK67dAVXlz9w4AAAoL6+HocOHZrx9927d2P37t0pjykUCvzoRz+a08DmG15c8j+acZtPuFFlIhSJ\n4cjblyERM5BLxbjUzxXtLiT7/IV3+/DBpQn8w/6bCt46KBS+mUGKSE6s5qwuTsCKRQxicVZoTd03\nxp1kjRU6FKllKFLLMDSZfWUpVLZIujCYeZGcp/SL3ROCQStP2YZqrtFjaNKLgQlP1uSkSWcACpkY\nj/75DXj4px/gleMD2NFSjqIs2yh8guCGRhPePD2C1l4bogUWDHf5wvj2L05hU5MJ9392Xd7jiauD\nPxgRktpicRaTjsC8lETj4W+CK6v1qC/X4nSXFafaJ9GUsFx4AxE8+otTuHN7LW7dXCU8b8oZhEYp\nhUohQUO5DmIRg9Y+G+75WOOM68GUa7qRSDot9cUYtfpQX65DRSICxXsX1zea5m2eEklq4h7VSSau\nV8qMKsikIrQPOOD2hVFlVs+472tVMlSa1OhNVL7g7yFdQw6EwjFs3GBKuZ/xYvdUhwUf31KFTFgd\nARTrFCmid3XCb/zq8QGEEz7Zt04PC13mdm2qSHmNSrMacpkYl0ddmHQGhGvF0fOjqC/XIRSJob4s\nu4YBAJVCglicRTgSn9P5zwvhmpLpSCsfPBue9Ga9n/M6waznroM1pVqsrjHgQq8NQxaPUB3D7g4K\nvRWMWexr2dCqpAAAd57GaYIneSHrJC8HeHHJ34zyRZJPtE3A7g7h9huqsbHJBI8/UlDZk0mHH6+f\nHILNHSooQ79z0IFnXmnHobd68hYbT55HaVokGeBu5gBn2gcgCI6+MTcYAHWJguM1pVrY3aGsyXS8\nSK5KiiQX6xRgmOms3UyEIzF4A5EZ5nt+RZyt8HecZWF1BFBiUEKtkOKzN9cjGI7ht+9lT67is2GL\nixS4ZX05HJ4Qjl0Yy3p8Mu39di5K0GcvuK0lsfDwUWT+JlVoN6VC6Rp2QCYVo75ch9W1BmiUUnzY\naUEszl1Eu4edcHhCaO2drmQRZ1lMuYKC6JXLxLhxdQnGpny4cHlqxntMl3+b2er0xjUlEIsYbGwy\nodSoAn/frSnVzCphJR8z6yRTJJm4PhGJGNSUaOHwhBCLs7h1S1VGH3FzjR7haBw9Iy6MWLnqT+cT\n5/eGFakLWFOREusajOgZcWW8xwdCUbj9ESFpj6dIw+0mhaNci+S6Mi3aBxz4oN2CUqNKiFDziEUi\nNJTrMG7z43TndNnad86N4vD/XAbDcG3rc6FWcNqAb2Q0WzLtKieL5GzwATuzfvozWJcoCfvCu33g\n77p2d0goXzvbSLLuKkWSrzuRvLmZF8m5b8DH2ybAALhtSzXW1XNfbnJr2Wy8dKwfsYTwGspTJsXh\nCeEnL17E+20TePP0MJ5+qQ0ubyjncywZIsmaxInAr954a4THH0YsHkf/hBsVZrUQ1eYjxKe7JjGV\nJHr9wQhefr8f3SNOFOvkwgkGcD+wYp0ip0jmBYIx7YbPr9x7xzJvUbu8YYSjcZQk2ol+dGMFtCpp\nilhJx+nhWl/rVDLcub0OMqkILx8fyGnp4OE7JvlD0YIapBBXB363Z11iOzRf047Z4A1EMGL1YVWt\nAVKJCGKRCDesKoHbHxEizHwd8OR2ti5vGNFYHOakyPCnt9cBAF45PjhjG9OW1kgkmboyHf7t6x/F\n5ouPTzAAACAASURBVGYzpBKR8HvfMI9RZGDabjHdlpqqWxDXL3yJNKVcgu1rMuet8CXX/vXQOXz7\nmVM48PsuXLg8BaVcgqaqmVVnbtvCidO3zgzP+Js1rfxbMmtquWvbHTfW4M7EdSQWZ3HrpsqMu9R8\ngOmNhC1r4woT3P4IJux+7NpYmRLIygTvGZ6rtZDXSck7emXFKkjEDAZy1Ci2pgXsAKAlkZOUfF+3\nu4OwJZLwjRmumbnQ8ZHkPM3KIgtdJ3m5wN+Am6r1UCskGMsRSbY4/Lg86sLqOgMMWjnW1hvBAGjL\n00t9yOLBB+0WISo0nEOAsSyL/3itA75gFLt3NeJzt9QjFmdxrDV3o48Jux9ymTil6DYfSZ4WydzN\n1+OPYNTqQzgSF7rmAdM+rQOvd+Eb/3ZC8Dw982oHXjzWD7FIhLt2Ns54b7NeCZc3nLGFZygSw0tH\nuVIzhrQVYbFOgSK1DL2jrozeKL6OIy/uJWIRSgzKxOo/sy/V4QmhSCODSMSgSC3DbVuq4fSG8XbC\n85QNlmVT2ormK+WTiUKEODF7+IXspubCdntmA+895qMZAHDjas7PznsVBxJ1wKecAeF3N5WIiCRf\n7CtNamxp5jzN7Wm/H/6mksluASBl+7UyceNZP49+ZGB6W3FGW2qKJBPXIXUJkXxzS3lWy8HaeiNK\nDUpUl2hQalTh6Pkx2NwhtDQYM0Yg1zUYUWpU4WS7ZYZIE8q/6WeK5E9vq8HujzXijptqsKnJBLNe\nAblMjB0tmcX7ioRAd/sjMOrk2Hsb1xdBKZfgc7fU5527KhEYm2skmQ/KJQt+vo7z0KQ3JciWDK9F\nTEnXwRKDSvhMJGIGpQYl7J4g7J7MwbV8aNV8JDn33KKJiltSiiTnZsLuh1ohgVYpRVmxCtYciUEn\nEl1q+MLhGqUUdeVaXB515WxC8PpJbrV37ydXQiYVYTCHPePouVG09duxrsGIO26qwW1bqiGTinD0\n/FhWC0CcZWFxBFBmUKVsGfGeZN4PWWqcjiTzFge+jSMAbGwy4e6PNmBLIju/LyFeu4acMBUp8IMv\nfyRjpQj+REk/MaKxOL574DReOz4As16Bm9eneqsYhkFjZRGc3rDgJU7GkuGiUqxTIM6ycGUoFh5n\nWTi9oZQt6jtuqoFcKsZbp4dzWihGp3xwecOCP6xjliL5XNck/vqJo0u+Ic21yLgQSS6GTCIqOJIc\nicbydsHjf3flSRGRpmo99BoZznRZEYnGMTDOLWpjcVbYFeHPKXOa6L1jWw2A6WsFwN0YznRZUWpU\nZbxBpvO5W+qx7xPNaCjP7SucLel2C/IkE9czN60pxRdvb84pKtUKKb7/V9vxnb+4Ed/64mbh/N24\nIvMuj4hh8PHNlYjGWHzYmdrB1+LgheXMkqRFGjk+dVMtZFIxRCIGD31hE/5+3xaoknZtk0kObq2p\nNcKkV+LLn2vB/75nvZC4lgt+N3iukeQJux96jWxGbtXWRCm3bI3CppwBaFXSGc9bl4gmN1QUodSo\nQiAUw1ii9fbsI8nc/NMbp6UzbbeYW53k60IkR2NxWJ0BlBVz4rK8WC0IznRYlsWJSxOQSUUpJZ5W\nVOoRi7NZe6m7vCF82DmJCpMa6xuLUW3WYNzmE0zj6VxKdPC6a2cDGIaBSiHBtjWlsLmDWStp2N1B\nRKJxQQTzCJFkPupVpAQDbvU5LZKnTzaJWIQ7t9cJF43RKR9cvjD8oShqSrVZkw35C0e65WLC7seI\n1YdNzWb8w/6bMgqERsFyMXOLhl95J/us+UjcVIamJ15/BLE4m9KdR6OUYvu6MtjcoZw2jfbEZ7t9\nbRkqTGp0jzhnVUWhc9ABFtPZucT8MWH3QynndknKilUYt/sL8oy/8eEwvnfwTM6FC38hLUr6zYgY\nBjeuLoUvGMW7F8ZSPPr8tYFfEJrSftP1ZTrIJCKMJFmqXjk+gFicxR/vqJtRBzUTVWYNPrY5s0fy\nSuDtFtMd9yiSTFy/SMQifHxLVcFJ9EVqGb6xdxM+f+sKQQxmYmUNF2gZTSS3RWNxTDr8wrXDnMFu\nkY5Jr0SlObtlQqWQCjtOfMLglpXmgrtz8nYL3xxEcjgSg80dSrF28mxuNkPEMPiwcxKjUz58/9kz\nQtfCeJzL4zBn0AEbm7hFx9p6oxA57kkEOGbrSdYopWAwXcUrG5S4VwDDk17E4qzwZfM/utEMJUzO\nX56C1RnEluYSKGTTJ1VVonzTaJbo1tELY4jFWXx8cyUYhkF1qRaxOJs1GjZs8YABhCx3APjoxkoA\nwLtZEtAsdu7kS//RahIiORzhfgxqpQRqpRQ2VxAXeqegkktS3oen1KiCWMRgzOYTxllhyt6Qg//R\np5eB46N0axuLs96IeZHePmCHxeFPWTxMOmZu6fCeTt7jmen9DGktLD+2ifv8/ufsCOJxVkgISKYt\nYbVYW2/E6hoDwpF41oRCHpsrKOwg2BILEf67IOaHWDwOi92PMqMaDMOgoliNSDSe0wPPw3uJe3KU\nAfT4OAGsT9vSu2lNKQDgt+9zSaI1CY8fv81oFRLxUqMcIhGDcpMaYzY/YnHu5vj+xQmUF6tw0+rS\nvGNeSMTpHfdIJBPErDDqFPjkjTU5k73KjEowDATr5pG3L2P/P72J91rHwQAo0c8uMpqNjU0mqOQS\nrKnP3mcgG/wusz80e5HMi/1MIlmrkmFVrR7942788PB59Iy4BN3CJ0lmEsnr6ovxrS9uxh031giR\nY74gwmztFiIRA41K+v+3d+eBUZVX/8C/d/aZTCYJ2SGQsIRVUHYUTVFRcUFUQEMwuLZoF+gLtUDV\nUrEu6BuXtsKL0lpZZKnGirbVX1FBQTZxYTMgJLIECNkgmclk1vv7Y+bezExmJpMEsky+n3+EkMDz\nmMydc889zzlyF69Q5JpklluE9ol3ytWoAZ67wl4+vRN9udxuvLPlGAQBuPVK/+lw0t1esN6ATpcb\nW74phV6rlMsUpMNxJ0LUJZ86V4ukeJ1fj8Pe6SakdjPg4I9VQbObwdq/AQ0vBIlBq4IpRoPKmnrU\n1jkwcVRG0MyWVPt7uqJODv6DBdOS5BCZZCloDfaikGSlm6AQBGz99jQWrdiJuX/6Ais2HUR1rQ3n\nqq3QqBV+7d6kILmqph52hwvrNv8gBy7ynPdY/8dNPVOMyM6Iw4GSKjyxchd+s+xLv/rj6lobio5X\nIyM5BgmxWnkE6MEwPbAt9Q488dddeHvzDwAaMovSIzW6OM5VW/1uZKXDMq+9tx/f/lCB59fsxZ/f\n3Re0pl26wQt3CDNYJhnw1Csmx+vkujYpaG6USQ5SY5yRHAOny42yKiu+2HcGblHEbVdFlkW+lKQ3\nA5fU3cLhgkataPd1EUUTtUqJ5Di9fA7h++PVUCkVSE80YPSglLDDsJrjzmv6oOAX4+XyguYwyOUW\nza9JDtYkwJcUT0nv/0UnqiGKYqP2b4H694yHWqWQM8duUUSsQd2i/18mg6bJg3tOHtwLr7rWhl2H\nypCeaMDQvp4DMg0BrH/Au33/WZyprMM1w7o36s8qZViDlVt8+nUpzpvtuOqydDn7LPUVDNbhwmz1\nTM0LnNYDeB6p2OwulAQ5OSoPEkkMCJL1/vVMeq2n9hrwBNA3ju7V6O9q2FcMrDYnvvc2Rg/Xl1bK\n9AYGyVLGNjFI2yuJVq1E7vX9MHZwKq66LA1GvRq7DpVh9ceHUXbeipR4/zprqdyi8kI9vj1agf9+\ndRL/2XUcAOShKcHaZl07wpNNlm4o9h1tKL34f3tOwOkS5d6Wg7MSoFIKcqufYI6VXoDN7sKJsw3T\nGgEELdUJVHHeijf//X2TL2Jq6GEsPXH4yfAeuH5EBkrLLfjTu/tw5NQFfPNDRaNyHc8jTs/34scw\nLRelNkGB40sFb8mFRPq1dBNUcaEecUZN0At4hs+N8+ET56EQhJA1jG1J6Q2Snd5SFbvDxSwy0SWQ\nnmhAbZ0DVTX1OF1hQXbPeDzz03EXtf++QiG0+DxBTCvKLc4EaTfra8SAZBi0KowemIKR/ZNRVWND\n+Xmrz6G98OUmvh2AmluPLIk1qGGpd4YtmXQ6u2gLuBqLHY+/sRPbmugG8cneU3C5Rdw4uqfcYiVG\np0ZSnA4nymrlzJRbFPH+thJoVApMubpxgb9Oo0JSnK5Ricbxs7V4Z8tRxBrUftnnjOQYCELwTHJD\naUOQIFk6UBZkmk/RyWqoVYpG2V69VgXfska9ViWf/LxpTK+QoyOBhszxgZIqCELou0bp7zXq1XJ7\nF4k0NSewbjPQxFE9Mfv2IXj4tsFY+siV6JcRh2+PVsBmd8mdLSTSC6iipl7OEB4s8dyphiq3AICx\ng1Lx8zsuw9MPj4VKKciP4M1WB7Z8exrxRo08XlyvVWFQZjecPGcOOSTlWKknKCu/YIUoinJmscZi\nhzXMIyxRFPH3j4rwxb4z2HHwbMjPC2S2OvD1kfJLNrGxo5Lq8KWDHQpBQN4N2Zj6kz4YkpWA6dd6\nuq189rX/JMYybwYa8GSj6+qd2F9cib0BB0pq6xyI0amCXiil8oiUBD0S43QwGdQ4V+XpcFFVY0Ny\niIu9FCQXn65ByZkaZKaFrudvS/LBPWdDn2QGyUQXn3QQePf35yCKQL8Ia4XbSkMLuFZkkhODxwQm\ngwYv/XI8HpkyBIOyGg7CB+uRHEyCb5Dcwj7x0uHFUDMfgIZyiy4XJO/6vgxnKuuw9/C5kJ9TV+/E\nlm9KEWtQy50qJL1SY1Fb58B5b/eEU+fMqK61YfTAlJCN/TOSjaipa5gK5nC68X/vH4DTJeLh2wb7\nHSTTqJVIT4zBiXPmRoePpMcz6UF++Ab0SoAANGotVXmhHqXlFgzKTGg0hlIhCPIpVqVCgEalwLjB\nqRjRPxkTRwWfCCSRRuU6XW4kx+vDjrgEPD/4Feet+OZIuXwDUF0TespYKIIg+N2MBPaU1GtVMGhV\nqPIZylJZU49z1VYUe7PswV6EgiBg1MAU9EiKQVaaCSfKzKi3O/Hp16dgs7tw05hefo9dpHZj3/xQ\n4clKBgTL0rRCq82F6lr/ASznwmST9x4ul8eWBnsqEIwoilix6SD+UrhfPgTRGblFEa8V7pcz/01x\nutz4/ngVkuN1cvtCwPO9vPXKLMzPHY5JY3ohrZsBe4rO+TWPP+O94ZR6AB85dR7L/3kAf/3XIb8b\njZo6e8jT4D2SY3DzuF64fXwWACClmwEVF+pRfr4eblFEUojHhtI5hS8PnIXLLWJAr47xBim9Gfj2\nSWaPZKKLT3oP3+lNhPTt0bincntqGCbS/Ezy2SrPaO1gpWYSjVoJQRAw0HuI8bujldhxoAxKhdDk\ntFTfJFdLM8lyh4swT2ul80/qrtbdYs/3nuA41EE6wPN4vc7mxI2jezZ6XCqVXEhZSulxr1SnGkyP\ngMN7nkNoVky4ojuG9mnc67RPdxNsdlejNZ6u8NyhBav/NerV6JUWKz/ml+w75ikJGNY3eE9V6bGK\nJ6ssYET/ZPzyrqF+hw+D8V1DuHpkSWo3PVxuEX8u3I/n1n4Np8uNqlobYnSqJv+tQIMzE+Q+kMEe\n6STG6VB5od5vqtHn+07jUEkV+vYwNZm5zu4ZB7cooujEeXyy9xRidCr85Ar/9nTD+yVBgKdX7tK3\nv8bvVuyUD/K5RVEOyAHIgauUtQ9Vl2xzuLDh0x+gUgrQaZRNHgyUfHesUq6P3lfc9OAaidPlbnGL\nn0iYrY4mpxr5OlFWi71HyvHhl8fhcDbdU7r4dA2sNpc8tCcYQRBw7XBPyyXfp0fSU5mRAzydaN7d\ncgz1dhfq7S75hsbtFmGuc8jN54P93dMn9JOfMKQm6D0/N94b1VCPDU0xGhj1avnfGdjRgmR3Q5/k\npm5+iaj5pPdMqayyb0bHuAZIdBolFILQ7PcHURRxtrIOKQl6v97uoaQnGhAXo8G3RytQWVOPSWN7\n+Z0xCkatajiH1NzOFpLYGM81PdzhPalffJfqblFVUy/3Ra3w6Tzgy2x14L9fnUSsQR10vrp0eE+q\nNS064XlDDJcNkoJk6fDed95WY+OGhGgE7r2rDOzh2pBJDh6UDs5MgMst4ojPaX3p3woZJHtrkPXa\n5r0Z+o7HberODwBuGZeJG0f3lG8AzlbWobrWhoRmzl0HPMHJzIn9MTgrIehNRqJJB5vDBUu9U56a\n9PGukxAB5AT0Yg4m23vB2vDpUdTWOZBzRfdGgXycUYu+PeJQfLoGx0pr4BZFfOydbnSmwgKrzQWl\n98CTFCRLzenLqurgdLkb/fztP1aJyhobrhuRgeyMeFRcqI+ol+P6T36AQhCgUgrYfyz84Bpfb3xw\nCAtX7Ahb/tEar77zHea/9iU+/PLHiNrl7fcO3bHanNgXsI9gZSRyqUUTp7fHD02DRq3A5r2n5HWc\n9r6WrvK+Bn1vSKX2gWarAyIams83Rcpmf7HPc1o7sEeyRBAEOZssCA0/b+1N6VNu4XaLcLlFaFr4\nBkFEofm+h6tVCr/xzR2B1F62ucNEaq0O1Nmcfk/2mvp3pNgpKU6H267KiujrpOC4WwviB8BnNHWY\nTHKXPLi325tFllqfSZlZXx/vPgGrzYVbxmUGzXBm+nS4cIsijpz0DNIIV2yekeR5AZSWWyCKIvYd\nq0CMToW+PYIPA5CD5FONg+RuJl3IWmGpvkea8Gd3uFB0vBo9kmJCrk96rGLQBs+WheI7Hjdc+zdJ\nRrIRuddnY5y3C8CRU+dRb3e1+E4wMy0Wv8kdHrTExbewf/TAFCTFeQaMaDVKjB4Uun+lROqQUFZV\n523+Hrz0ROqHPTw7CT1TjNh7uBwVF6zyIbEh3uDtiPf7KD1aKqu2Ytl7B7Do9Z1+weN+bxZ4zKBU\n9E73/JyVNJFN3nu4HOeqrbh2eA8M6JWAU+XmoINXApVV1WFP0TmYrY6wXTpayi2KOH62Fk6XG4Wf\nF2PZewearJc+6JMF33XI8xiy4rwVy97bj7l/2tbopvFgSSWUCiHsUxzAc1J7whU9UF1rw5feIR6n\nK+qgVSsxoFeCXA8s3dRIdebSDUqkp8OlWuMS73CRcKNfpc/NDNNfvK0pBAFKhQCn2w27N5PPTDLR\nxWfQqRDnnX6bkWyUD812JAatqtmZ5KbqkYMZPTAVKqWAWZMGRHwGQiqzSGzxwT1poAhrkv3sKToH\nhSDghtGe+emlFY07SOw6VIYYnUrunRso3qiByaDGiTIzTp0zw1LvbLKmMC3R01e4tMKM0nILqmps\nGNK7W8jHEWmJBsToVPJIXACotztRWWNDz9TQb7wDesYj1qDG9v1nYLU5UXSiGnanW+7OEYxRL5Vb\nNP/NUOobHSqzHYx0xywN7mhp4X04vjXOmWmxcrA6dlBqRKUdMTq1nP0fNTA5ZN3T9SMz8Ku7huLR\nOy7DjaN7wi2K+H97TsrBnHRDID3az86Ih0IQsO9YJb49WoELZrvcz1kURRwoqfJMaUyLlbs1NFWX\nLJVuXJ6diKHefR4oabrkYvPehoNs4bp0tFStxQ6nS8SQrAQM6BmPb49WhD2IWFfvxNHSGvTtbkJ6\nogHfHq3Ev3b8iMdX7sJXh8thtjqw/J8H5MC1ts6OH8/Uom93U0RBpqd3qYB/7zgOh9ONs1V1SE80\nQKEQkOl9TUklNVImWWrvFhui3CLQsL6J+NXUofjV1KH4wwOj0TvMRDwpgJZunDoKlVIBp0uE3dm6\nLAoRhZfuLRWUnjB2NJ5McvOCZLmTVpiD/IFGDkjGsnk/CVs2F+iKfknolWKU36ebyySVW4R5Uut0\ndrE+yWarAyVnatC/ZxwGyRNv/Gt+bQ4XKi7Uo2eKMWQGRRAE9EqLRWVNPd7ZegxA0290KqUCPZJi\nUHK6Fhs/OwoAuDxMyyeFdxzzufNWXPA+DpBak/VMDf2CUquUuH5kBupsngNn72wpBgB5jHQwUia5\nJdmsm8f2wi3jMuWShkhIQbI01jnUYcfW8M1OZ6bGIufy7uiVasRNY3pG/HcMyeoGAZBvqIJRqxQY\n3j8ZKqUCYwenIs6oweavTmHbvjPQqpWNvsdJ8TokxeuCHuI7XWFBda0Ng7MSoFAIcoBV3ESQLHfs\niNXJN0NS2UIodfVObNt/BgmxWsQbNdh3rBIud+TTAyNR4Q3+eyQb8dCtg6BVK7Fu8w+4YA6e5f7+\neBXcoojL+iRi3OBUOF1uvLu1GHqNEj+dPBh35fRBda0Nb2w6CFEU8b13guGQIOU2wSTEanH1sO44\nd96Kv/7rEJwut1wmNOXq3rh9fBZyLg8Mkj2vvUjGuAKelkvDs5MxPDtZLssKZdSAFORcno7rRga/\nGW8vKqUAp8sNu3ckNcstiC4NqcNFc94/21KMTuV3LYjE2ermB8lA87O144em4w8PjmnxU7iIDu51\ntUyydIirb484uTwgcKqd9Kigqczo5KuyYNCq5LKGARG0b7n3xgFQqxRyy7RgtbS+pNOuUjZZmi7T\nKy10dgoArhuRAY1agcKtxThVbkbO5d3DnpyVapINLfhh69sjDtMm9JVb5EXCoFMj0aSVT4629HRq\nOFImOSlOB6Nejd7pJvzhgTHNynjfmdMHSx4ag77dIzt1rFIq8PCtgzFmUAr69jDh5rG9oNeq/A4h\ndIvVyrVaGrXnJST9zEmBrfRzEWvQIDleh5LTNWHLFKQguVusFmndDEg06XCopCps0PvlgTOw2V24\nbkQPXJGdDLPVIbesC+U/O4/j+TV7G9UWV1ywysMnfFXVeNaVaNIhKV6PaRP6wlLvxKbtPwb9+6X9\nX9anG666LB1GvRrDs5Ow5KGxuHJIGm65MhNDenfDwR+rcbT0gvzaa6oe2dct43oh1qCWy66kIHlA\nrwTccU0fueuJb7s+oHGP5IvBoFPh/psHNdkTtK0pvZlk6fXJcguiS2PMwBRkpcU2GQu0F4NPh4u6\negfcPu9Dod6TWpJJbg9G79PBcC3gnF1tLPWPZz1BQO90Eww6NRJitY27R4RpseYrOyMev79/FPp0\nN2FQZkKT3RIAoF9GHOZOGwaNSoGBvRLkuuiQnx9weK/EG+Q3dcjHqFcj5/LuEAGkxOuRe32/sJ/v\n292irfRMabhzvhSZ5NQEA1RKQe6A0RJatVKelhipIb274ZEpl+Hx/FG43dumTgq8dBol9FoVMtNi\nIQC465o+ABoyyQe9JRJDfIK+3ukmWOqdYQeQVNfa5L9bEAQM6Z2AOpuz0cAbX0XejizjBqfJQyy+\n+aE85OcDntaJR05dwGGfFnMnymqx8P92Ys1HRY0+XxqeIt2wXDu8B+KMGuw6VNaoc4XD6ca3P5R7\nbmjSTEiM0+HVOVfjV1OHyQGqQhAwaaxnuM22fWdwoKQSRr26WVmYpDg9np99JR694zLcPLYXrhmW\n7vfncj9v79qlerVQ3S2ikVopwOVyy6PqWW5BdGkM6JWA398/+pK8B14MUmxw6Mcq/OqVL/DrP23D\n/67/BvNf247Hln8ZtF65rNrqmbfQwa+Zeq0KAsK3uJO6W3SZcosfvYdppPqfHkkxqK61+TXLlu6C\nIsk4piQY8MSsUfhN7hURr2FgZgKe/dk4/OLOpqfq9PGOY5Y6I5ScroFKqUBmmDpHya3jMjF6YAoe\nveOyJmtwjfqWl1u0lO+BpktxgTDq1Xhi1ijkTex/0f/u5pJGbCbG6SEIAm67MhNPPzwWV3sDtHPn\nrbA7XDh88gJ6pRj9emYPzvIEzN8cCR3AejqENHyNVPojdV0J5sezNTDFaNDNpMWgzARoNUq5RjwY\ntyjK5T7f/tBQv7zNO1L5/+063ijDLNVaSwcrFAoBVw1JQ53NiW9+8K+B3nnwLGrqHLhmWLo8AlkI\n8nRiUGYCEk1afHngLM6b7RjSu1uznmIAnp/z0QNTMP3afkHLKJLidKi8YIVbFJtdbhENlEoFHC53\nQyb5Io3IJaLORcokf/HdaYjwZI8P/VgNs9WBqhpbo4PUbreIc9V1SOtmCHr97kgUggC9VhV2WIpD\n7m7RRfokl3gDAymgCOxdDABn5CA58kcFzf1h8HSnaPouS6tRom8PE0rO1KC61oZT5Wb0SjVGlNmJ\nM2rx6B2XRZRl65FshIDwJ/Evtl4+/1ZLW7g0+W+kxjaZrW8LUiZZGiyhUSvRPSkGBp0aRr0aZdVW\nlJypgdPlbtSlYUT/ZCgVAnYXBR98Y3d4evr6Bcnev6Po+Hm4RRFvbz6Cr3y+vsZiR1WNDVlpsRAE\nAWqVAv26m3Cmsi5ku5/qGpucWfz2hwqIoginy41d35fJf+d3R/2D7MBMMgBcNdRzY7B9f8MBPrco\n4qPdJ6BUCJg4KnzNuEIQcNVl6fKkvOaUWkQqKV4Pp0vEBbO92Qf3ooFKqYDLJcLmZE0yUVcmZZJ/\nOHUBSoWAF39+Ff7y6xz8/A5Pki/wUHlFTT2cLhFp3TpWCVkoMfrwBxOlcouWdh7pVFfOCwGBAdBQ\nj+gfJFugVSs7zOOPoX0SIYrAf3Ydh8stoncT9cgt0TPFiD//+hqMGhD6cN/FJgXkMTpVi2fLdxZS\nkJwYpPY0NcEzhVAqfwgspTHq1RiUlYDjZ2sbTfQDgGqzdGiv4ec13uipTT5y6jy+OVKOzV+dwvvb\nSuQ///Gs/xMVAOjt7aQhPW0JdKaq4TVSWVOPU+UWHCipQm2dA4O8Qfk2b29gSVVNPTRqhXyhBTxP\nb3qnm3CgpBLnvWs/UFyJM5V1GDs4NaLX3XifEokhlyBIlnobV1ywoqbODkFoqNvvCqSDew4Ha5KJ\nujKp1awIT/mnTqOCQaeSD5UHBsmdpR5ZYtCpw/aBdrjcUCqEZj+tlEQUJFdWVmLChAkoKSlBVVUV\nfv7znyM/Px95eXk4efIkAGDjxo2YOnUqcnNzsWXLFgCAzWbDnDlzMHPmTMyePRvV1aEfHUfiuLce\n2TcwSO/mCZKlg1Nut4izVVakJXacRwVSQf/Wbz0BSFb6pTkFa9Cp23TPKfF6xOhUQaflRRup0kFa\nvwAAIABJREFUH26wTH1KgmcK4c5DnoxsdpAa6tEDPX2dvwqSTa6uaehs4Wtgr3jY7C6s+e8RAJ4b\nQalLyo/ya6Hhhku6+QrVSUO6+En1y3sPn8N27/S6aRP6ol9GHPYXV/l1rqisqUeiSdfo5+rqYekQ\nRWD1x4dx3mzD25t/AADcGKaLiK+UeD1yLu+OcUNS/UpTLpYk+fBePWotnpHULb1IdkYNLeA8mWTW\nJBN1TTE+T7wH+yQkTDEaJJp0KA44VC7FUp3lfT1Gp4Ld4Q456MrpdLfq+tfkVzqdTixevBg6necN\n/MUXX8Ttt9+O1atXY+7cuSguLkZFRQVWr16NDRs2YOXKlSgoKIDD4cC6devQv39/rF27FlOmTMGy\nZctavFCgIUPm27c0xftIQDo45XlU4G5WqcWl1ivViLgYjVwfGK7vameiUAh4bMZw/PS2we29lEsu\nMy0Wj88aidtz+jb6M6nTRVlVHVK7GYJ2UZBKLrZ8U4pN20r87t59O1v4kkouLpjt8oAMaVSy9FrI\nDJJJDjW45Iz34nfD6J5QCAI2bf8Re4+UI62bAVlpsZg4uhfcoiiXhVhtTljqnUEbvV8zLB2DMhPw\nzQ8VePyNnThXbcWtV2Y22TLN1/03D8TPJg+J+PObI8mbSS6/YEVNmJHU0UqlkFrASTXJDJKJuiLf\noWWBpW29u5tgtjrkdplAQ5vazpRJBkIf3nO43C1u/wZEECQvXboUM2bMQEqKJxP29ddf4+zZs3jg\ngQfw4YcfYuzYsdi3bx9GjhwJlUoFo9GIrKwsFBUVYe/evcjJyQEA5OTkYMeOHS1eKBD8EXOsXg29\nVil3DjjjLbtI70DfYEEQ5GyyTqNs1hSbjq5XamynueNsrb7d44JOEkpJaCjBCJZFBjx381f0S0LF\nhXr8c1sJlq79Wr4YVdV6LlCBZQoDfPp233GNp8uG1Jf6eFkt4oyaRiUaCbFalJzxZAaKjlcHPdDa\np7sJt1yZieyMOIy/LA0P3DIQgiBgzBBPCYTUlq3KG7wnBhnLrFIq8Is7hyIjOQZWmws/uaI77srp\nE3Tv7UEKks9W1sFqc3apQ3tAQ7sjq3dkOsstiLomKZMco1PJk4YlfYKUXEjvS5GOpG5vUimgJUQb\nOKfrEmaSCwsLkZiYiPHjx0MURYiiiNLSUsTHx+PNN99EWloaXn/9dZjNZsTGNvzPNxgMMJvNsFgs\nMBq9dasxMTCbQ7ezaorV5sT3J6qRFKdDnM/jWUEQkJJgwLlqz0n2M83obNGWpAERWWmxXeqxb1eQ\n4nMxCRUkA8DPbh+MJ+8bhRnXZ8PudGPlh4fgcrt9Bon4B8lxMRoMyUrAwF7xmOTt11x0vBoXzDZU\n19qC1rb3STfhgsWOD7/8ES+s+wbPr/1a7iF5ptKCRJMWWrUSd+X0waJ7R+Kh2wbLNdTJCXr0SIrx\nTHh0uORDe6F6YBt0Kvw2bwR+cedlyL9xQIcpbwI8QbJCEOQSmK50aA9oaJxvtXmDZGaSibqkOKMG\nguA5+yF1HZL09pZ+BgbJ3UzaTnPOSLoJCDV62+kSoVK2/L0pbL+wwsJCCIKA7du34/Dhw1iwYAGU\nSiWuvfZaAMB1112Hl19+GUOHDvULgC0WC0wmE4xGIywWi/wx30C6KcnJ/p/7nx0/wmZ3Ydp12Y3+\nLDPNhONnayGoVaj21mwOzk5u9HntaUKsDl/sP4ObxmbK6+pI67tYonFPgQL3qI9pCG7HDeuB5DB9\nmbunx2PMsB44U23Flq9P4YsDZbDYPHWj2b2TGpVqPP+rHIiiCEEQMKxfEnYdPIvPvvPUEQ/um9Ro\nLZdlJ2PvkXK894XnkN+pcgv+VLgfTzwwBufNdgzvH/51MeaydLy35SjKauywe8vUemfEh/yaZAC9\ne138g3cXw//kjcDHO3/EoZIqXD4gJapfdxJpbwbpkKLCExwnJRmjYt/RsIemRPMeo3lvko62x+Tk\nWDzz6Hj0So31SzACgNGkh0L4Bqcq6pCcHIt6mxPVtTZcnt34vUX6uzqaFG9CVKVVBV2f0yXCaFC3\neO1hg+Q1a9bIv541axaeeuopvPLKK9iyZQumTJmCPXv2IDs7G0OHDsXLL78Mu90Om82G4uJiZGdn\nY/jw4di6dSuGDh2KrVu3YtSoUREvrLy84YS+KIr44PNjUAgCRvRN9PszoGFIwPdHy3GopBIalQIa\niI0+r73Nm345AM/ekpNjO9z6Wisa9xQo1B5NBs+hSZXojuj/wdSc3vjq+zIUfnYUcUYN1CoF6i31\nsNUFH/cMAH3SY7Hr4Fls+qIYWo0SgzJMjf6tFJ9R3ndf2w+nKy3Ytu8MHl+2HQCQGKsNub7k5Fj0\nTfME+F98c1IuLVF3wNdSJIb0jMOQnpfD7RahUAhR+7qT+O7N5T3/UOkdL2u12Dr9vqP5eyeJ5j1G\n894kHXWPaSYt7FY7yq2NxzenJ8Xgh5PVKDtXI08EDvY+0VH3Jro8SaYzZbUoT25cQWB3uiAAYdce\nLoBu9uSJBQsW4IknnsD69esRGxuLgoICxMbGyt0uRFHEvHnzoNFoMGPGDCxYsAB5eXnQaDQoKCho\n7j8HwHNa/+Q5M0b2Tw7aXirVWxN67HQNSsstGJSZ0KpCbaLmevSOy6BUKiIuOYjRqXHNsHT8Z9cJ\nmK0OpCTom/za4f2S8P4XJeiXEYdZNw0IWgaRlRYLjVqBtG4G3DA6A4Cn17FUyxzJFEqtWolvf6iQ\n+1MHq0nuTAIfMXYF0vWvTiq3UPN6SESN9UwxorTcgvLzVpRWeILkHkkdq1w1HKncwhyiDZzT6W7x\ntD2gGUHyqlWr5F//7W9/a/Tn06dPx/Tp0/0+ptPp8Oqrr7Z4cZJt3jZVE0b0CPrn0sGx7fs9nzeg\nZ/iRz0QXm+8hu0hNGN4DH+06ARFAQgRt0JLi9fjTr68JW9Ou16qw+P7RMMVooPQ+an9kyhAs+ftX\nqKypR1oTtfpqlQKDMhPw7dEKVFyox8Be8ZdsUAxdOlINnlSTrObEPSIKQgqIS8stOF3hefLUvVMF\nyZ4wNlhNslsU4XKLrUqatt0M41Y4VnoBWrUSg0IEIlJ3AakN3IBeDJKp40uO12NY30R8d6wSCabI\negVHcugz8NBqrEGDefdcjr2HyyO6gbxpTE84XG5cMywdowam8KBpJyRnkut5cI+IQpNmAJwqN+O0\ntztYZwqSG1rANc4ku7y9k1WtuP51+CDZ4XThTGUdstJjQz429bSBU8Fqc0KlFKKmDzFFv4mje+K7\nY5WXvBtLemIMbrsqsn9jQK+EFmXGqeNgdwsiikSP5IZMcmmFGbEGdadqmRkukyzNpmiTcov2crqi\nDi63iF4poQurBUFAaoIeP56tRZ90E3uCUqcxJKsbFt8/Oqp6Z1P7k8otGmqSeU0kosYSTTroNEqU\nnKlB5YX6TvckXs4kB+mT7HB5WjS1JpPc4dMLJ855TiT2TA3dVgtoqEvu38m+wUSZabFBh5QQtZQy\noNyCY6mJKBhBENAjOQYVF+ohwtPtojPRa5VQCAIstsaZZKecSW55yWCHv3JKLUnCZZIBzwlNwJOZ\nIyLqyqQ3BZdbhEIQ2O2HiELK8Ont35k6WwCeIN+gUwUtt3BKNcnRXG5x4pwZgtBQNxPKxJEZ6J8R\nj35hJp4REXUFvm8KbP9GROH4BsbdO9i04kgYdKqgB/ccF+HgXoe+eoqiiJPnapHWzdDk42iNWskA\nmYgIDeUWAA/tEVF4vpnk7k0kJDuimBCZ5ItxcK9DXz0rLtTDanPJpRRERNQ0lU8NHnskE1E40pN6\no14NUyfqbCEx6NRwON2wO1x+H5fKLVpzJqNDl1ucPOetR07tePPCiYg6KpZbEFGkYg0aDOndDSnx\n+vZeSotIbeAs9U6/Tj7Swb2orUkuLfcEyb6PAoiIKDzfTLKGmWQiasL8e65o7yW0mDSauq7egYTY\nhsFccgu4aO1uYfHWmJhi1O28EiKizsM3c6JmJpmIopjBJ5PsSy63iNaa5Hq7Z8N6TYdOeBMRdSgq\nHtwjoi4iJsRoaungXtR2t7DaPEXYOg0fFxIRRYrlFkTUVYQaTd0FMslSkMxMMhFRpJQ8uEdEXYQ8\nmjogSI76Psn1dicEgRd5IqLmUCl8W8Dx+klE0csgZ5L9yy2c0d4n2WpzQadRQRBafjKRiKir8c2c\nsNyCiKKZNGzO7g2KJU65u0WUBsn1difrkYmImkml8OluwUwyEUUxqdogcJiIw+n5vUoVpS3g6u0u\nBslERM3kd3BPzWsoEUUv6Rpnd/hnkqU+yVFbbuEJknloj4ioOfzLLTr0ZZ6IqFW03muc3Rl8LHVU\nHtxzutxwutzQa5kFISJqDt9yCwbJRBTN1KrgmeQ2O7hXWVmJCRMmoKSkRP7YBx98gNzcXPn3Gzdu\nxNSpU5Gbm4stW7YAAGw2G+bMmYOZM2di9uzZqK6ujnhhbP9GRNQyLLcgoq5CrkkOlUm+lEGy0+nE\n4sWLodPp5I8dOnQI7777rvz7iooKrF69Ghs2bMDKlStRUFAAh8OBdevWoX///li7di2mTJmCZcuW\nRbywepun3x1rkomImsf38SIP7hFRNFMpFVAqhMY1yVIm+VKWWyxduhQzZsxASkoKAOD8+fN45ZVX\n8Pjjj8ufs2/fPowcORIqlQpGoxFZWVkoKirC3r17kZOTAwDIycnBjh07Il6Y1c5pe0RELeFXbsFM\nMhFFOY1a0bi7xaXOJBcWFiIxMRHjx4+HKIpwuVx4/PHHsXDhQuj1evnzzGYzYmNj5d8bDAaYzWZY\nLBYYjUYAQExMDMxmc8QLq7d7Msl6LcstiIiaQ6nkMBEi6jo0KiVsIfokt+YaGDYCLSwshCAI2L59\nO4qKinD77bcjIyMDf/jDH2Cz2XDs2DE899xzGDt2rF8AbLFYYDKZYDQaYbFY5I/5BtJN0eo1AIDE\nBAOSkyP/us4kGvcVjXsKFM17jOa9SaJ5j9LeRFGUP5aSZIyaPUfLPsKJ5j1G894k0bzHjrw3vU4F\np9Ptt0aFN4OclmpqccI17FetWbNG/nV+fj6efvppZGVlAQBKS0sxf/58LFq0CBUVFXjllVdgt9th\ns9lQXFyM7OxsDB8+HFu3bsXQoUOxdetWjBo1KuKFlZV7gm6Xw4Xy8toWbK1jS06Ojbp9ReOeAkXz\nHqN5b5Jo3mPg3lRKBZwuN+ostqjYczR/7yTRvMdo3pskmvfY0femVAiotTn91mipswMAzldbYA5T\nchEu+I84tBYEwS874SspKQn5+fnIy8uDKIqYN28eNBoNZsyYgQULFiAvLw8ajQYFBQWR/nOw8uAe\nEVGLqZQCnC62gCOi6KdRKYOMpXZDgCeAbqmIg+RVq1b5/b5Hjx5Yv369/Pvp06dj+vTpfp+j0+nw\n6quvtmhhbAFHRNRynsMqLtYkE1HU06oVcDjdcIsiFIInKHa6RKhUCghCFI6llg7u6ThMhIio2aRe\nyVp2tyCiKCd18XH4tIFzudx+PeNbouMGyTa2gCMiaimp7REzyUQU7dRBRlM7XG4oFa27/nXYq6ec\nSWa5BRFRsym9QbJGxUQDEUU3TZDR1C6XGMWZZG9Nsp6ZZCKiZlN73xzU6g57mSciuii0QUZTO93u\nVg0SATpwkNzQ3YKZZCKi5lKrlNCoFfIhFiKiaCXVJPtmkp0usdVBcoeNQOs5lpqIqMXuyumD82Zb\ney+DiOiS03gzyTaf0dROpxsqQ+uSBB06SNaoFVC0or8dEVFXNaR3t/ZeAhFRm5BrkgPKLZTRWm5R\nb3dCz1ILIiIiIgojWLmFyyVCHa1BstXuYqkFEREREYUllVvYveUWblGEyx3V3S2cPLRHRERERGFp\n5XILTybZ5fL8NyrLLVxuEXaHG3pO2yMiIiKiMAIzyU6XCABQtfJcW4cMktn+jYiIiIgioQ7IJDu9\nmWRVKyeOdswguV4KkplJJiIiIqLQtKEyydFYbmG1OQAwSCYiIiKi8AK7W8iZ5Kgut9Cy3IKIiIiI\nQtN4yypsTimTHMUH9+pYbkFEREREEWjIJHuCZJe33CIq+yTz4B4RERERRaJRuYVbyiRHc7kFM8lE\nREREFIZUbiEf3HNG8cE9S73n4J6BNclEREREFIbcJzmwBVxUZpKlmmQOEyEiIiKiMJQKBVRKoSGT\n7JaC5DbIJFdWVmLChAkoKSnB999/j5kzZ2LWrFl4+OGHUVVVBQDYuHEjpk6ditzcXGzZsgUAYLPZ\nMGfOHMycOROzZ89GdXV1RIuSDu7pWZNMRERERE3QqJQ+meQ2KrdwOp1YvHgxdDodRFHEs88+i9//\n/vdYtWoVbrjhBrzxxhuoqKjA6tWrsWHDBqxcuRIFBQVwOBxYt24d+vfvj7Vr12LKlClYtmxZRIuq\n89Yk61luQURERERNUKsVPjXJbXRwb+nSpZgxYwZSUlIgCAJefvllDBgwwLsIJzQaDfbt24eRI0dC\npVLBaDQiKysLRUVF2Lt3L3JycgAAOTk52LFjR0SLqvPWJDNIJiIiIqKmaH0zyW1RblFYWIjExESM\nHz8eouhJXSclJQEAvv76a7z99tu4//77YTabERsbK3+dwWCA2WyGxWKB0WgEAMTExMBsNke0KPZJ\nJiIiIqJIaXwyyS653KJ1meSwqdrCwkIIgoDt27ejqKgICxYswPLly7Fr1y6sWLECr7/+OhISEmA0\nGv0CYIvFApPJBKPRCIvFIn/MN5AOx2pzQhCAjO7xULRypGBHlpwc2f+PziQa9xQomvcYzXuTRPMe\no3lvQPTvD4juPUbz3iTRvMeOvrcYvQZl1VYkJ8dCb6gEAHSLN7Rq3WGD5DVr1si/zs/Px5IlS7Bt\n2zZs3LgRq1evhslkAgAMGzYMr7zyCux2O2w2G4qLi5GdnY3hw4dj69atGDp0KLZu3YpRo0ZFtKi6\negd0GiUqKyPLPHdGycmxKC+vbe9lXFTRuKdA0bzHaN6bJJr3GM17A6J/f0B07zGa9yaJ5j12hr0J\nEOFwulFWVoPq81YAQF2dvcl1hwuiIy76FQQBLpcLzz77LLp3745f/OIXEAQBY8aMwS9/+Uvk5+cj\nLy8Poihi3rx50Gg0mDFjBhYsWIC8vDxoNBoUFBRE9G9Z6p2ctkdEREREEZGn7jldDX2SW1mNEHEk\numrVKgDArl27gv759OnTMX36dL+P6XQ6vPrqq81elLXegViDptlfR0RERERdj+9oajlIVkXhxL26\neif0HCRCRERERBHQ+oymlg/utTKT3CGDZJdb5CARIiIiIopIQ7mFGw6X1Cc5CjPJAKBjj2QiIiIi\nioBG7c0kO30yydEaJOvZI5mIiIiIIqBWBalJvtQT99oLp+0RERERUSS06oaaZKc7yjPJnLZHRERE\nRJHQeDPJNocLTiczyURERERE0Gp8yi3cUpAcpZlkBslEREREFAmtt7tFvcMFZ9Qf3GOQTEREREQR\nkDLJNrsLrqg/uMeaZCIiIiKKgE7KJNud7JNMRERERAT4ZJJ9J+4xk0xEREREXZnOp9zC6XJDEACl\nIkozyaxJJiIiIqJIBB7ca+2hPaADB8k6DYNkIiIiImpaYCa5taUWQEcOkrUstyAiIiKipmnkg3ue\nILm1pRZABw2S9VoVFELr7wCIiIiIKPqplAqolAr54J5aFaVBskHHUgsiIiIiipxOo/SUW7jdUCqi\ntNyCQTIRERERNYdWrfSUWzjd0Xtwz6BVt/cSiIiIiKgT0WqUsMndLdook1xZWYkJEyagpKQEJ06c\nQF5eHu6991489dRT8uds3LgRU6dORW5uLrZs2QIAsNlsmDNnDmbOnInZs2ejuro6okXpmUkmIiIi\nomaQM8nuNsokO51OLF68GDqdDgDw3HPPYd68eVizZg3cbjc2b96MiooKrF69Ghs2bMDKlStRUFAA\nh8OBdevWoX///li7di2mTJmCZcuWRbSoGB0zyUREREQUOZ1GCafLDYejjYLkpUuXYsaMGUhJSYEo\nijh06BBGjRoFAMjJycGXX36Jffv2YeTIkVCpVDAajcjKykJRURH27t2LnJwc+XN37NgR0aJYk0xE\nREREzSENFBHR+pHUQBNBcmFhIRITEzF+/HiIomcOttvtlv88JiYGZrMZFosFsbGx8scNBoP8caPR\n6Pe5kejdPa7ZGyEiIiKirksaKAIAyouQSQ6bsi0sLIQgCNi+fTsOHz6MBQsW+NUVWywWmEwmGI1G\nvwDY9+MWi0X+mG8gHc7ka/qgvLy2JfshIiIioi5I6xMkqy91kLxmzRr517NmzcJTTz2FF154AXv2\n7MHo0aPx+eefY9y4cRg6dChefvll2O122Gw2FBcXIzs7G8OHD8fWrVsxdOhQbN26VS7TiERycmQB\ndWcWjXuMxj0FiuY9RvPeJNG8x2jeGxD9+wOie4/RvDdJNO+xM+wtIU4v/9pgULd6zc0u/l2wYAGe\nfPJJOBwO9O3bF5MmTYIgCMjPz0deXh5EUcS8efOg0WgwY8YMLFiwAHl5edBoNCgoKIj434n2THJy\ncmzU7TEa9xQomvcYzXuTRPMeo3lvQPTvD4juPUbz3iTRvMfOsje30yX/2uV0R7TmcIF0xEHyqlWr\n5F+vXr260Z9Pnz4d06dP9/uYTqfDq6++Guk/QURERETUIr7lFqponbhHRERERNQcOrVPkKyK0ol7\nRERERETN4Z9JZpBMRERERAStuqGKWNlWY6mJiIiIiDoy3z7JbTJxj4iIiIioo/Mrt2AmmYiIiIio\nYSw1wEwyERERERGAwEwyg2QiIiIiIr8WcDy4R0REREQE/0yymplkIiIiIiJAo1JAyh8zk0xERERE\nBEAQBDmbzJpkIiIiIiIvBslERERERAGkw3vsk0xERERE5MVMMhERERFRADmTrGAmmYiIiIgIAKDV\nqAAASmaSiYiIiIg8pHILtYpBMhERERERgIZyCyXLLYiIiIiIPHqlGmHQqtDNpGv136W6COshIiIi\nImp3E0f1xLUjekCpaH0euMkg2e1244knnkBJSQkUCgWeeuopOJ1OLF68GCqVCllZWXjmmWcAABs3\nbsSGDRugVqvxyCOPYMKECbDZbHjsscdQWVkJo9GI559/HgkJCa1eOBERERFRoIsRIAMRlFt8+umn\nEAQB69atw9y5c/HSSy/htddewy9/+UusXbsWNpsNW7ZsQUVFBVavXo0NGzZg5cqVKCgogMPhwLp1\n69C/f3+sXbsWU6ZMwbJlyy7KwomIiIiILpUmg+SJEyfi6aefBgCUlpYiLi4OgwYNQnV1NURRhMVi\ngUqlwr59+zBy5EioVCoYjUZkZWWhqKgIe/fuRU5ODgAgJycHO3bsuLQ7IiIiIiJqpYjy0QqFAgsX\nLsQzzzyDyZMnIzMzE8888wxuvfVWVFVVYcyYMTCbzYiNjZW/xmAwwGw2w2KxwGg0AgBiYmJgNpsv\nzU6IiIiIiC6SiA/uPf/886isrMS0adNgs9nw9ttvo2/fvli7di2ef/55XHPNNX4BsMVigclkgtFo\nhMVikT/mG0iHk5wc2ed1ZtG4x2jcU6Bo3mM0700SzXuM5r0B0b8/ILr3GM17k0TzHqN5b6E0mUl+\n//338frrrwMAtFotFAoF4uPjERMTAwBITU1FTU0Nhg4dir1798Jut6O2thbFxcXIzs7G8OHDsXXr\nVgDA1q1bMWrUqEu4HSIiIiKi1hNEURTDfYLVasWiRYtQUVEBp9OJn/3sZ4iPj8eLL74IlUoFjUaD\np59+Gt27d8c//vEPbNiwAaIo4tFHH8XEiRNRX1+PBQsWoLy8HBqNBgUFBUhMTGyr/RERERERNVuT\nQTIRERERUVfDiXtERERERAEYJBMRERERBWCQTEREREQUgEEyEREREVGAdg+S8/PzUVJS0t7LuOhK\nS0sxcuRIzJo1C/n5+Zg1a1bIkdyd5f/B7t27MXDgQPz73//2+/jkyZOxaNGidlrVpfPGG2/g6quv\nht1ub++ltFpX+94Bned11VLh9nfdddd12p/baHrdBfP666/jgQceQH5+Pu677z4cPHiwvZd0UZ06\ndQpz5szBrFmzkJeXhyVLlsizEgKdOXMGn332WRuvsOV2796NUaNGoaysTP5YQUEB/vnPf7bjqi6O\n3bt346qrrpJjlhkzZuA///lPey+r3UU8TISaLzs7G6tWrWrvZVxUffr0wb///W/ccsstAIAjR46g\nvr6+nVd1aXzwwQe47bbb8K9//Qt33nlney+n1brS966rEwShvZfQYtH2uvN17NgxfPrpp1i/fj0A\noKioCAsXLoyKIAsAbDYbHn30UTz77LMYOnQoAOCf//wn5s+fj//7v/9r9Pk7d+5EcXExrr322rZe\naotpNBosWrQIf/vb39p7KRfdlVdeiYKCAgBAXV0d7r33XvTu3RsDBw5s55W1n3bPJANAVVUVHnnk\nETz00EOYPHkyPvnkEwDA7bffjj/+8Y9yJrazjbQO1l3vpZdewsyZM5Gbm4uPP/5Y/virr76K++67\nDz/72c9QXV3dlstsloEDB+L06dPy92LTpk24/fbbAQBr167Ffffdh3vuuQePPPIInE4n3nvvPdx7\n772YOXMmdu7c2Z5Lb5bdu3cjMzMTubm5ePvttwF4MneLFy9Gfn4+8vPzUVlZid27d+Puu+/Gvffe\ni02bNrXzqsNrzvfO4XBg/vz58iCgY8eOYfbs2e229pb685//jA0bNgAAiouLkZ+fD6DzX1skofbX\nWTt7hnrdSRnz9evX4y9/+QsA4LXXXsNdd92Fhx56CDNnzsSePXvabd2RMhqNOHv2LN555x2UlZVh\n4MCB+Mc//oEjR45g1qxZmDVrFubMmQOz2Yzdu3fjwQcfxEMPPYQ77rgDa9eube/lN2nLli0YO3as\nHCADwB133IHz58/j+PHjyM/PR25uLh544AFUVlbi9ddfx7/+9a9OlU0eN24c4uLiGn0ChvT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            "text/plain": [
              "<matplotlib.figure.Figure at 0x117326a20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "51xHSrdQpRgc",
        "colab_type": "text"
      },
      "source": [
        "In particular, the striking feature of this graph is the dip in birthrate on US holidays (e.g., Independence Day, Labor Day, Thanksgiving, Christmas, New Year's Day) although this likely reflects trends in scheduled/induced births rather than some deep psychosomatic effect on natural births.\n",
        "For more discussion on this trend, see the analysis and links in [Andrew Gelman's blog post](http://andrewgelman.com/2012/06/14/cool-ass-signal-processing-using-gaussian-processes/) on the subject.\n",
        "We'll return to this figure in [Example:-Effect-of-Holidays-on-US-Births](04.09-Text-and-Annotation.ipynb#Example:-Effect-of-Holidays-on-US-Births), where we will use Matplotlib's tools to annotate this plot.\n",
        "\n",
        "Looking at this short example, you can see that many of the Python and Pandas tools we've seen to this point can be combined and used to gain insight from a variety of datasets.\n",
        "We will see some more sophisticated applications of these data manipulations in future sections!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbNXoBWOpRgc",
        "colab_type": "text"
      },
      "source": [
        "<!--NAVIGATION-->\n",
        "< [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) | [Contents](Index.ipynb) | [Vectorized String Operations](03.10-Working-With-Strings.ipynb) >"
      ]
    }
  ]
}